Homomorphic Encryption

The present work builds on previous investigations of the authors (and their collaborators) regarding bridges, a certain type of morphisms between encryption schemes, making a step forward in developing a (category theory) language for studying relations between encryption schemes. Here we analyse the conditions under which bridges can be performed sequentially, formalizing the notion of composability. One of our results gives a sufficient condition for a pair of bridges to be composable. We illustrate that composing two bridges, each independently satisfying a previously established IND-CPA security definition, can actually lead to an insecure bridge. Our main result gives a sufficient condition that a pair of secure composable bridges should satisfy in order for their composition to be a secure bridge. We also introduce the concept of a complete bridge and show that it is connected to the notion of Fully composable Homomorphic Encryption (FcHE), recently considered by Micciancio. Moreover, we show that a result of Micciancio which gives a construction of FcHE schemes can be phrased in the language of complete bridges, where his insights can be formalised in a greater generality.

In this paper we study monoid homomorphic encryption schemes over (F2, •). Such encryption schemes occur naturally by forgetting the addition operation in a Ring Homomorphic Encryption scheme over F2 (if it exists). We study the structure of such schemes and analyze their security against quantum adversaries. We also present the only two monoid homomorphic encryption schemes over (F2, •) that exist in the literature and we raise the question of the existence of other such schemes. For one of the two schemes we present experimental results that show its performance and efficiency.

We present a new method that produces bounded FHE schemes (see Definition 3), starting with encryption schemes that support one algebraic operation. We use this technique to construct examples of encryption schemes that, theoretically can handle any algebraic function on encrypted data.1

We analyze the structure of finite commutative rings with respect to its idempotent and nilpotent elements. Based on this analysis we provide a quantum-classical IND-CCA attack for ring homomorphic encryption schemes. Moreover, when the plaintext space is a finite reduced ring, i.e. a product of finite fields, we present a key-recovery attack based on representation problem in black-box finite fields. In particular, if the ciphertext space has smooth characteristic the key-recovery attack is effectively computable. We also extend the work of Maurer and Raub on representation problem in black-box finite fields to the case of a black-box product of finite fields of equal characteristic.

Distributed Federated Learning (DFL) extends federated learning from a single-server topology to broader graph-based coordination settings that may include multiple interacting aggregation nodes and, in some deployments, externally distributed training resources. While this broader topology improves scalability and flexibility, it also enlarges the poisoning attack surface across participants, aggregators, communication channels, and execution resources. This paper presents SecureDFL, a defense-in-depth framework for poisoning-resilient DFL. SecureDFL integrates authenticated communication, protected aggregation based on additive homomorphic encryption, trust-aware orchestration, multipoint validation, and reinforcement-learning-based mutation of resource mappings under a unified system design. The contribution of this work therefore lies in the integrated security architecture and its end-toend implementation for graph-based DFL, rather than in introducing a standalone cryptographic primitive. Experiments on benchmark datasets, seven threat-model-aligned poisoning scenarios, ablation studies, and a prototype-scale Kubernetes testbed show that SecureDFL preserves strong model performance under attack and substantially improves attack resilience relative to the evaluated baselines, achieving up to a 98.5% reduction in attack success rate in the reported settings with only modest additional overhead. These results support the practical feasibility of the proposed architecture, while the manuscript explicitly delineates its current limits regarding comprehensive collusion analysis, colluding trust manipulation, extremely heterogeneous non-IID conditions, and fully adaptive multi-stage attacks.

Machine learning had been widely used to analyze various kinds of data, including sensitive data such as medical and financial data. A trained machine learning model can be wrapped in a web application so that people can access it easily via internet. However, if the data to be analyzed is private or confidential, this will cause a problem; the application administrator may read the input. As shown by Dowlin et al. in their remarkable paper, this kind of problem can be solved with homomorphic encryption scheme. Paillier encryption scheme is one kind of encryption scheme that has homomorphic property. In this research, we will show that one type of machine learning model can take an input encrypted by Paillier encryption scheme and produce an encrypted output that shares the same key. A machine learning model will be trained with the MNIST database of hand-written digits. This model will be tested with the test data encrypted with Paillier encryption scheme. The experiment shows that the model achieved 92.92% accuracy on the test set.

Customer Rela onship Management (CRM) systems increasingly rely on ar ficial intelligence to generate insights, automate campaigns, and personalize user experiences. However, deploying large language models (LLMs) across mul cloud infrastructures introduces significant challenges in data privacy, interoperability, and model governance. This paper presents a federated LLM framework for CRM automa on that enables organiza ons to collabora vely train models without sharing sensi ve customer data. The approach integrates cross-cloud federated orchestra on, encrypted communica on pipelines, and domainadap ve personaliza on modules for region-specific customer behavior modeling. Experiments conducted across heterogeneous datasets illustrate the scalability of framework, accuracy, and robustness, achieving low latency, strong differen al privacy guarantees, and efficient cloud-edge deployment. The proposed system bridges enterprise-scale data analy cs and privacy-preserving genera ve intelligence, seDng a founda on for responsible AI-driven CRM ecosystems.

Post-quantum cryptography relies on precisely stated computational hardness assumptions rather than on physical analogy or structural complexity alone. Motivated by latticebased cryptography and by aperiodic cut-and-project geometry, this manuscript defines a deliberately conservative toy construction called a cut-and-project one-way map (CP-OWM). The construction combines a public modular linear map with a nonlinear acceptancewindow predicate derived from a hidden perpendicular projection. The purpose is not to introduce a deployable encryption scheme, key-encapsulation mechanism, or signature algorithm. The purpose is narrower: to test whether a nonlinear aperiodic window predicate can produce measurable resistance to standard lattice-reduction recovery attacks beyond matched SIS/LWE-style baselines. We formulate the toy primitive, state the adversarial knowledge model, identify correctness and leakage risks, and define failure criteria. Preliminary benchmark logs indicate that uncalibrated window scaling can make the acceptance region too loose, causing the CP-OWM instance to remain as vulnerable as a baseline under BKZ-20 attack. We therefore introduce acceptance-rate calibration: the window bound is tuned dimension by dimension so that the valid-sample probability remains close to a fixed target, here approximately one percent. This does not prove security. It only creates a more controlled benchmark in which attacker recovery probability, legitimate valid-key generation cost, entropy, collision rate, and leakage can be compared at fixed acceptance probability. The manuscript should be read as a feasibility and falsification protocol. A positive benchmark would motivate further cryptanalysis; a negative benchmark would show that the proposed aperiodic predicate collapses to known lattice-reduction behavior or leaks too much information. No claim of post-quantum security, standardization readiness, or blockchain protection is made. The calibrated acceptance-window constraint did not reduce attacker recovery probability relative to the SIS-style baseline; under the tested LLL/BKZ configurations, CP-OWM remained fully vulnerable up to N = 60. The proposed toy construction therefore does not currently provide evidence for a post-quantum-secure primitive.

Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys.   However, most MFHE schemes require a Common Random\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext.  Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext.  Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently. In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS.   To this end, we remove the CRS by designing a new relinearization algorithm....

Digital image watermarking plays a vital role in authentication, identification, and copyright protection. It is essential that the watermarking technique used is robust enough to resist various types of attacks. These attacks are commonly applied to evaluate the resilience of different watermarking methods. As a result of such attacks, the embedded watermark may be degraded or destroyed, leading to potential copyright issues. This study investigates the impact of several attacks on watermarked images and the extraction of the original watermark. A comparison is made using various performance metrics, including Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Metric (SSIM) of the extracted watermark. The results show that, under Gaussian noise, the similarity of the extracted watermark is only 2.85%, whereas the watermark extracted after applying a Gaussian LPF retains 100% similarity with the original.

The increasing adoption of smart cameras and image sensors in industrial and medical applications necessitates robust visual data security solutions. The industrial Internet of Things (IoT) introduces unique security challenges, particularly due to third-party involvement, which undermines traditional security mechanisms. This study presents a three-layered encryption scheme integrating novel blockchain technology with chaotic fractal image encryption scheme to address these challenges. The encryption process combines an S-box generated from the May map for pixel substitution with fractalbased key generation using a logistic map-driven Sierpinski triangle and incorporates a Chebyshev map-based diffusion step for enhanced randomness and security. Extensive testing, including key sensitivity analysis, entropy calculations (average entropy: 7.9998), NPCR (99.92%), UACI (33.31%), and PSNR values (29.74 dB for encrypted images), validates the scheme's robustness. The results confirm high resistance to differential and brute-force attacks, making the scheme highly suitable for securing sensitive medical images in IoT environments while ensuring confidentiality and integrity.

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing hematological malignancy that requires early and accurate subtype identification to enable effective treatment planning and improve patient survival outcomes. The classification of ALL subtypes is a complex multi-class problem due to high morphological similarity among leukemic cells and significant variability in patient data across different healthcare institutions. Traditional centralized machine learning approaches for leukemia diagnosis face major limitations, including data privacy risks, regulatory restrictions, and lack of generalization across heterogeneous clinical environments. This study proposes an Adaptive Federated Learning framework integrated with Differential Privacy for early and accurate identification of ALL subtypes using distributed medical datasets. The proposed system enables multiple healthcare institutions to collaboratively train a global deep learning model without sharing raw patient data. Each institution trains a local model on its own dataset, while an adaptive aggregation mechanism dynamically adjusts client contributions based on data quality, distribution characteristics, and training performance. To ensure strong privacy protection, Differential Privacy is incorporated into the federated learning process by adding controlled noise to model updates before transmission. This mechanism prevents sensitive patient information from being inferred while maintaining high model utility. The adaptive nature of the framework improves learning efficiency under non-IID data conditions commonly found in real-world healthcare systems, where data distributions vary across institutions. The classification model is designed using deep learning techniques capable of extracting discriminative features from microscopic blood smear images, enabling accurate differentiation between multiple ALL subtypes. Experimental results demonstrate that the proposed framework achieves high accuracy, precision, recall, and F1-score while significantly enhancing privacy preservation compared to traditional federated and centralized learning approaches. The findings confirm that adaptive federated learning with differential privacy provides a scalable, secure, and clinically viable solution for early leukemia subtype detection in distributed healthcare environments.

The industrial 3D mesh model (3DMM) plays a significant part in engineering and computer aided designing field. Thus, protecting copyright of 3DMM is one of the major research problems that require significant attention. Further, the industries started outsourcing its 3DMM to cloud computing (CC) environment. For preserving privacy, the 3DMM are encrypted and stored on cloud computing environment. Thus, building efficient data masking of encrypted 3DMM is considered to be efficient solution for masking information of 3DMM. First, using the secret key, the original 3DMM is encrypted. Second without procuring any prior information of original 3DMM it is conceivable mask information on encrypted 3D mesh models. Third, the original 3DMM are reconstructed by extracting masked information. The existing masking methods are not efficient in providing high information masking capacity in reversible manner and are not robust. For overcoming research issues, this work models an efficient data masking (EDM) method that is reversible nature. Experiment outcome shows the EDM for 3DMM attain better performance in terms of peak signal-to-noise ratio (PSNR) and root mean squared error (RMSE) over existing data masking methods. Thus, the EDM model brings good tradeoffs between achieving high data masking capacity with good reconstruction quality of 3DMM.

This third volume advances QRAM-hardness cryptography, arguing that the ultimate quantum resilience of lattice-based primitives must be grounded in the thermodynamic lower bounds governing coherent state preparation, rather than transient algorithmic assumptions. While Learning with Errors (LWE) is mathematically susceptible to the Gottesman-Kitaev-Preskill (GKZ) quantum learning algorithm, this paper definitively proves that the index-erasure problem and the construction of a necessary Quantum Random Access Memory (QRAM) oracle impose insurmountable physical barriers.

The necessity of reversing classical computation paths to maintain quantum coherence is subject to Landauer’s Principle, dissipating a minimum energy of $k_B T \ln 2$ per erased bit. For cryptographically relevant dimensions, the total dissipated energy scales to macroscopic limits, instantly vaporizing cryogenic cooling systems and physically preventing the required $\left|\psi_{s, e}\right\rangle$ superposition state preparation. Furthermore, this heat manifests as non-Gaussian thermodynamic noise, pushing continuous-variable GKP error-correction codes beyond their critical correctable threshold and inducing catastrophic logical failure.

The resulting framework introduces QRAM-hard primitives, including Thermodynamic-LWE (T-LWE) and Enclave-Oracle PUFs, which maintain classical efficiency while forcing any quantum adversary into this physically prohibitive regime. Security is thus transitioned from an algorithmic assumption to a physical guarantee anchored in the fundamental irreversibility of the universe.

In this research we present our implementation and testing of XOR encryption and decryption blocks for cognitive Very High Frequency Land Mobile Radio (VHF LMR) communication networks on GNU Radio platform. VHF LMR systems utilize 136-174 MHz band of radio spectrum. XOR encryption is an efficient method for securing data in such digital radio communications. We demonstrate how this can be achieved in cognitive VHF LMR networks. Specifically, we model our system, develop and test XOR encryption and decryption blocks. The results that were obtained indicated a successful encryption and decryption process thus demonstrating the viability of XOR operation in cognitive VHF LMR networks. In addition, this research provides a foundation for development and testing more complex encryption schemes there by enhancing security of spectrum sensing in cognitive VHF LMR networks communications

A new method of secure data aggregation for decimal data having integer as well as fractional part using homomorphic encryption is described. The proposed homomorphic encryption provides addition, subtraction, multiplication, division and averaging operations in the cipher domain for both positive and negative numbers. The scheme uses integer matrices in finite field Zp as encryption and decryption keys. An embedded digital signature along with data provides data integrity and authentication by signature verification at the receiving end. The proposed scheme is immune to chosen plaintext and chosen ciphertext attacks. In the case of homomorphic multiplication, the ciphertext expansion ratio grows linearly with the data size. The computational complexity of the proposed method for multiplication and division is relatively less by 22.87% compared to Brakerski and Vaikantanathan method when the size of the plaintext data is ten decimal digits.

Research for greater computing power, always present in the computation that led to the creation of parallel architectures consisting of thousands of processing units, as occurs in the architectures of GPUs. In this context, this work presents a search for using the computational power of GPUs through CUDA architecture, aiming to solve computational problems found in the class NP-Complete, those that can take years to resolve. This paper shows three strategies to approach with their limitations and benefits. With these results, there is evidence that the architecture is efficient in solving these problems.

Early detection of cardiac rhythm disorders (arrhythmias) poses a significant challenge, especially when using wearable IoT devices that continuously monitor ECG signals. Conventional cloud-based deep learning methods are largely infeasible due to considerable latency and raise serious data privacy concerns by transferring sensitive health information to central servers. In this paper, we propose a novel system using federated fog computing for real-time arrhythmia detection and classification while protecting patient data privacy. This proposed system deploys compact deep learning (DL) models on proximate fog nodes, such as smartphones or home gateways, in close proximity to the user. Instead of sending raw ECG data, models are trained locally, and only model updates are sent to a cloud server for aggregation to avoid data leakage and reduce latency. We create a lightweight hybrid model that combines an attention mechanism with convolutional neural networks (CNNs) and long short-term memory (LSTMs), specifically designed for low-power fog devices. Our proposed model achieves a classification accuracy rate of 99.06, precision of 99.04, recall of 99.06, and a weighted average F1-score of 99.04 on the public MIT-BIH arrhythmia dataset. The experimental results show that the inference latency is significantly reduced to 0.22 ms (cleartext), 1.24 ms (encrypted) per sample/heartbeat inference time, and the encryption throughput reaches 577 beats/s using the homomorphic encryption technique. This approach is a suitable alternative for real-time ECG monitoring within a federated fog computing framework because it strikes a fair balance among speed, accuracy, and privacy.

An encryption scheme is homomorphic if it supports operations on encrypted data. Homomorphic encryption allows a device to perform arbitrary computations on encrypted data without user secret key. Recently it is introduced new homomorphic encryption schemes with improved performance that can be implemented in IoT device in production environments. The IoT concept encompasses devices, sensors, and services existing within an interconnected infrastructure with an efficient access to sample computational facilities. In this paper we evaluated features of exact arithmetic homomorphic encryption mechanisms: BFV and BGV and approximate homomorphic encryption scheme: CKKS. In the paper we measured performances of operations of homomorphic encryption schemes: BGV, BFV and CKKS that are implemented in Raspberry Pi 4 IoT device.

Fully Homomorphic Encryption (FHE) was initially introduced as a concept shortly after the development of the RSA cryptosystem, by Rivest et al. [54]. Although long sought after, the first functional scheme was only proposed over thirty years later by Gentry [34, 35] in 2009. The same blueprint to construct FHE has been followed in all subsequent work. First a scheme is constructed which can evaluate arithmetic circuits of a limited depth, a so-called Somewhat Homomorphic Encryption (SHE) scheme. If the complexity of the circuits which the SHE scheme can evaluate is slightly more than the complexity of the decryption circuit for the SHE scheme, then (by placing a SHE encryption of the scheme’s private key inside the public key) one can bootstrap the SHE scheme into a FHE scheme. This bootstrapping operation is obtained by homomorphically evaluating the decryption circuit on input of the ciphertext to be bootstrapped and the encryption of the secret key.

Cloud computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user.

Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution for predicting energy consumption while preserving user privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking, and the data exchanged between peers is encrypted using Cheon-Kim-Kim-Song (CKKS) fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers contribute further towards developing a next-generation SG IoT system.

The Internet of Medical Things (IoMT) is transforming healthcare by enabling live monitoring of patient health parameters, remote diagnostics, and seamless medical data exchange. However, the growing interconnectivity of medical devices also presents significant security and privacy risks, including cyber risks such as breaches, intrusions, and threats. To mitigate these concerns, this article introduces a comprehensive and customized security management structure to address the security challenges in IoMT environments. The framework incorporates encryption, authentication, and intrusion detection mechanisms to protect sensitive medical information and facilitate secure communication between connected devices. By utilizing advanced security protocols and risk assessment strategies, it enhances the robustness of IoMT structure against emerging cyber threats. This study assesses the framework's effectiveness by examining existing security models, regulatory standards, and cutting-edge technologies including blockchain and AI. Key findings emphasize the necessity of adaptive security policies, real-time threat monitoring, and adherence to healthcare regulations like HIPAA and GDPR. Additionally, the research identifies challenges related to scalability, interoperability, and resource limitations in implementing IoMT security measures. By addressing these challenges, the approach ensures a well-defined structure for identifying and addressing risks, ensuring patient data privacy and reinforcing trust in IoMT-based healthcare solutions. This study serves as a valuable guide for healthcare providers, policymakers, and cybersecurity experts looking to elevate the overall security posture in IoMT ecosystems.

Modern cryptography relies heavily on algebraic structures such as finite fields, elliptic curves, and lattices. This paper explores a novel conceptual framework for cryptographic transformations based on Grassmann algebra. Grassmann variables exhibit anticommutation and nilpotent properties that differ fundamentally from classical numerical structures. We investigate how these properties can be exploited to design unconventional encryption transformations using Grassmann polynomials and supermatrix operations.

The Internet or distributed computing has evolved from a simple file sharing mechanism to data source sharing and dynamic services. This evolution has made data source sharing an urgent necessity at the present time. Therefore, we should benefit from distributed data sources that are spread across a network and address current data sharing challenges, which include masking the heterogeneity between data sources, and between disparate clients and different communication protocols and formats. In the recent past, efforts have been made by researchers and private companies to propose approaches for accessing remote, heterogeneous and autonomous data sources to share them across a network. Other efforts are looking at these approaches and concepts with the aim of developing applications in both centralized and decentralized environments to provide uniform access to and sharing of data sources. In this paper, we review four data sharing approaches that have been proposed, namely Transaction Processing Monitors, Tuplespace, Resource Description Framework and Data Service Approach. For each approach, we will present its architecture, limitations and problems, as well as applications that have been developed based on its concepts. Moreover, the most important open problems related to data sharing systems are briefly highlighted.

To prevent unplanned machine downtime in production, machine conditions can be monitored and even predicted using condition and failure models based on current machine and process data. As most of these models are data-intensive, machine users often do not have enough data to develop these models themselves and want to collaborate with other companies. Since these models often require critical and classified machine and process data, which could be extracted from the models using attacks such as model inversion, sharing existing models between companies is not an option as it leaves one party vulnerable. Privacy preserving technologies such as homomorphic encryption, differential privacy, federated learning and secure multi-party computation can help overcome this problem. With the help of these approaches, there is no need to transmit sensitive data unencrypted to third parties in order to cooperate and take advantage of high-performance models. The aim of this paper is to first su...

Human resource data accuracy has become a significant factor in assessing the effectiveness and performance of human resource management (HRM) in businesses. Numerous human resource hazards originating from asymmetric information continue to cost firms money and even put companies out of business despite the rapid progress of mobile technology and Internet technology. Blockchain is a transaction platform that makes use of the immutability qualities of immutable data records. Because of the dispersed nature of this technology, it has a broad variety of applications in numerous industries. Seeing the potential of this new technology, we picked the HRM sector since this data must be kept private and secret while still having great research value. This research presents a unique blockchain-based encryption method for establishing an HRM data method that decreases the danger of HRM data integrity. The data is authenticated using smart contracts, and data is encrypted using the proposed Improved Identity-based blowfish encryption algorithm (IIBEA) with Particle Swarm Optimization (PSO). New blocks are verified using the Proof of Work (PoW) consensus process. The suggested model's metrics are examined and compared to traditional encryption approaches. This model solves the lack of difference in the authenticity of HRM information, and it will give real and effective information to the HRM of organizational companies.

The increasing capabilities of mobile devices have given raise to many mobile sensing applications. The problem with existing system of the mobile sensing applications is that the sensor data aggregation assume a trusted aggregator, and hence cannot protect user privacy against untrusted aggregator. An untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. To address this problem, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead

This paper addresses the problem of data sharing among multiple parties, without disclosing the data between the parties. We focus on sharing of data among parties involved in a data mining task. We study how to share private or confidential data in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively construct support vector machines using a linear, polynomial or sigmoid kernel function. To tackle this problem, we develop a secure protocol for multiple parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private. We analyze the protocol in the context of mistakes and malicious attacks, and show its robustness against such attacks. All the parties are treated symmetrically: they all participate in the encryption and in the computation involved in learning support vector machines.

We present an approach for performing the tallying work in the coercion-resistant JCJ voting protocol, introduced by Juels, Catalano, and Jakobsson, in linear time using fully homomorphic encryption (FHE). The suggested enhancement also paves the path towards making JCJ quantum-resistant, while leaving the underlying structure of JCJ intact. The pairwise comparison-based approach of JCJ using plaintext equivalence tests leads to a quadratic blow-up in the number of votes, which makes the tallying process rather impractical in realistic settings with a large number of voters. We show how the removal of invalid votes can be done in linear time via a solution based on recent advances in various FHE primitives such as hashing, zero-knowledge proofs of correct decryption, verifiable shuffles and threshold FHE. We conclude by touching upon some of the advantages and challenges of such an approach, followed by a discussion of further security and post-quantum considerations.

Fully Homomorphic Encryption (FHE) allows computation on encrypted data. Various software libraries have implemented the approximate-arithmetic FHE scheme CKKS, which is highly useful for applications in machine learning and data analytics; each of these libraries have differing performance and features. It is useful for developers and researchers to learn details about these libraries' performance and their differences. Some previous work has profiled FHE and CKKS implementations for this purpose, but these comparisons are limited in their fairness and completeness. In this article, we compare four major libraries supporting the CKKS scheme. Working with the maintainers of each of the PALISADE, Microsoft SEAL, HElib, and HEAAN libraries, we devise methods for fair comparisons of these libraries, even with their widely varied development strategies and library architectures. To show the practical performance of these libraries, we present HEProfiler, a simple and extensible fram...

E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, and coercion resistance. Traditional centralized systems lack transparency, while fully decentralized models face scalability and privacy issues. This paper proposes a hybrid architecture compliant with OSCE/ODIHR standards for low-trust environments. The protocol decouples identity from voting: an off-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as an immutable bulletin board. Utilizing homomorphic encryption, Zero-Knowledge Range Proofs (ZKRP), and Distributed Key Generation (DKG), the system ensures End-to-End Verifiability (E2E) by delegating tallying to auditable scripts. Finally, the study examines model limitations, specifically regarding endpoint vulnerabilities and physical constraints on coercion resistance.

This paper presents an original approach to encrypting digital data using mathematical fractals. We begin with a detailed classical fractal-based encryption approach inspired by the Sierpiński triangle, including step-by-step construction and decryption procedures, and then extend it to a post-quantum secure method using lattice-like transformations. The proposed methods leverage the self-similarity of fractals and the computational hardness of lattice problems to enhance security and diffusion. Complete decryption processes are explained for both approaches, accompanied by Python prototypes and a full encryption/decryption flow diagram.

Private Set Intersection (PSI) protocols allow a querier to determine whether an item exists in a dataset without revealing the query or exposing non-matching records. It has many applications in fraud detection, compliance monitoring, healthcare analytics, and secure collaboration across distributed data sources. In these cases, the results obtained through PSI can be sensitive and even require some kind of downstream computation on the associated data before the outcome is revealed to the querier, computation that may involve floating-point arithmetic, such as the inference of a machine learning model. Although many such protocols have been proposed, and some of them even enable secure queries over distributed encrypted sets, they fail to address the aforementioned real-world complexities. In this work, we present the first encrypted label selection and analytics protocol construction, which allows the querier to securely retrieve not just the results of intersections among identifiers but also the outcomes of downstream functions on the data/label associated with the intersected identifiers. To achieve this, we construct a novel protocol based on an approximate CKKS fully homomorphic encryption that supports efficient label retrieval and downstream computations over real-valued data. In addition, we introduce several techniques to handle identifiers in large domains, e.g., 64 or 128 bits, while ensuring high precision for accurate downstream computations. Finally, we implement and benchmark our protocol, compare it against state-of-the-art methods, and perform evaluation over real-world fraud datasets, demonstrating its scalability and efficiency in large-scale use case scenarios. Our results show up to 1.4× to 6.8× speedup over prior approaches and select and analyze encrypted labels over real-world datasets in under 65 sec., making our protocol practical for real-world deployments.

Due to the enhanced digital media on the web, information security and privacy protection issue have attracted the eye of information communication. Information hiding has become a subject of sizable im-portance. Currently each day there's very big drawback of information hacking into the networking space. There is variety of techniques offered within the trade to over-come this drawback. So, information hiding within the encrypted image is one in all the solutions, however the matter is that the original cover can't be losslessly recov-ered by this system. That’s why recently; additional and additional attention is paid to reversible information concealing in encrypted pictures however this technique drawback low hardiness. A completely unique technique is planned by reserving for embedding information be-fore encoding of the image takes place with the offered algorithmic rule. Currently the authentic person will hide the information simply on the image to produce authen-ti...

The proliferation of cloud computing and the exponential growth of big data have revolutionized the way organizations store, process, and analyze data. However, the benefits of cloud-based big data come with significant security challenges, as sensitive information is stored and processed in remote data centers operated by third-party providers. This abstract provides an overview of the crucial role encryption plays in enhancing the security of cloud-based big data environments. Cloud-based big data environments involve the storage and processing of vast datasets across distributed infrastructure, often accessible from various locations and devices. This diversity and accessibility introduce potential vulnerabilities that can be exploited by malicious actors. To mitigate these risks, encryption is a fundamental security measure that safeguards data both in transit and at rest.

This study proposes an artificial intelligence-driven secure semantic communication system. Its goal is to enable safe, real-time communication while preserving the meaning of messages. The system uses advanced encryption methods, such as AES-based semantic encryption and homomorphic encryption, together with reinforcement learning (RL) to dynamically adjust encryption. The system architecture comprises several modular tiers. Data preparation, semantic analysis, dynamic encryption adjustment, and safe transmission are all parts of these layers. To test the system's encryption, security, and adaptability, extensive research was conducted across different network conditions, including excellent, middling, and poor connections. The results show that the system can maintain low latency in real-time communication, maintain both data secrecy and semantic integrity, and dynamically adjust the encryption level based on real-time network conditions. The system's capacity to keep communication private shows that it has all of these features. This solution is perfect for applications that require secure communication with minimal delay. Some examples of these uses include mobile communications, critical infrastructure, and networks for the Internet of Things.

E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, and coercion resistance. Traditional centralized systems lack transparency, while fully decentralized models face scalability and privacy issues. This paper proposes a hybrid architecture compliant with OSCE/ODIHR standards for low-trust environments. The protocol decouples identity from voting: an off-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as an immutable bulletin board. Utilizing homomorphic encryption, Zero-Knowledge Range Proofs (ZKRP), and Distributed Key Generation (DKG), the system ensures End-to-End Verifiability (E2E) by delegating tallying to auditable scripts. Finally, the study examines model limitations, specifically regarding endpoint vulnerabilities and physical constraints on coercion resistance.

An emerging technology in data analytics, Cloud Computing (CC) is used to store, retrieve, and distribute data in a dispersed setting. Both individuals and businesses save their data on cloud servers. However, data privacy and security have become a rising problem that limits the organization from employing cloud services. In order to overcome that problem, the Paillier encryption method is proposed to offer safety for healthcare cloud data. Creating a pair of keys offers a comparatively easier method to ward against attacks and safeguard data confidentiality. Following data selection, the Paillier methodology is used to encrypt the input. The encoded health data were then kept in cloud environments, where they provided better readwrite storage operations. The Paillier method is used in the data decryption procedure to get the data from the cloud environment. A Recurrent Neural Network (RNN) classifier is employed in healthcare diagnosis to categorize and forecast a patient's ailment, after which the diagnosis is communicated to the patient and physician. The proposed work uses Python software, and a comparative analysis is conducted. An effective prediction using RNN showcases an accuracy of 96.4% with a better performance matrix. After classification, the outcomes are conveyed to patients and doctors for treatment.

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual’s privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. Availability and implementation: Download HEALER ...

The simplicity of sharing data through the Web furthermore, Distributed computing accidentally presents a developing issue of Data leakage. In the meantime, numerous end-clients are unconscious that their information was leaked or stolen following most information is leaked by operations running out of sight. This paper presents a novel client driven, mantrap-propelled Data Leakage Prevention (DLP) approach that can find, present any sending of information – both approved and unapproved – to end-clients and along these lines give them the capacity to stop the sending process. Our proposed system introduces mechanism to design an algorithm which communicates Internet of Things devices with the corresponding privileges and present a novel approach to support stronger security by encrypting the data with differential privilege keys also reduce the storage size of the authenticated information to increase the security of the data while sending to agent. Keywords—Internet of Things (IoT)...

Encryption is the process of disguising text to ensure the confidentiality of data transmitted from one party to another. Homomorphic encryption is one of the most important encryption-related processes which allows performing operation over encrypted data. Using different public key algorithms, homomorphic encryption can be implemented in any scheme. There are many encryption algorithms to secure operations and data storage, which after calculations can obtain the same results. While there is a considerable contribution and enhancement in the field of homomorphic encryption for various performance metrics, there is still an necessity to clarify the applications dealing with this technology. Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption. Recently, many distinguished research papers have been filed to address the need for various applications of homomorphic encryption. Example of these applications but not limited to : Vehicle to Vehicle (v2v) secure communication, cloud security, Vehicular ad-hoc networks (VANET), Blockchain, E-Voting, Data mining with privacy preserving and healthcare sector. This article aims to introduce a literature survey to close the gap in homomorphic encryption systems and their applications in the protection of privacy. We focus on abovementioned applications and present our recommendations for future work.

Cloud computing is a new paradigm of information technology and communication. Performing big and complex computations in a context of cloud computing and big data is highly appreciated today. Fully homomorphic encryption (FHE) is a powerful category of encryption schemes that allows working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing powers. Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This article aims to introduce an efficient fully homomorphic encryption scheme based on a new mathematic structure that is noise free.

Performing smart computations in a context of cloud computing and big data is highly appreciated today. It allows customers to fully benefit from cloud computing capacities (such as processing or storage) without losing confidentiality of sensitive data. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that enables working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing capabilities. While FHE is combined with verifiable computation, it offers efficient procedures for outsourcing computations over encrypted data to a remote, but non-trusted, cloud server. The resulting scheme is called Verifiable Fully Homomorphic Encryption (VFHE). Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently hand...

Treasure Token is a secure and dynamic digital reward system integrated within a lucky draw platform, designed to offer users an engaging and profitable experience. Each Treasure Token represents a lucky draw ticket, uniquely identified with its own ID to ensure transparency, traceability, and fairness in the draw process. The system prioritizes user account security through robust authentication and encryption mechanisms, ensuring safe participation and secure financial transactions. Payments are handled with end-to-end encryption, and automatic tax deduction ensures regulatory compliance with minimal user effort. Treasure Token incorporates a "profit-guaranteed" reward mechanism where treasure values may vary, but users always stand to gain. The platform supports secure and seamless withdrawal of winnings, along with a refer-and-earn feature that rewards user engagement and network growth. Additional features include real-time analytics of ticket performance, fraud detection algorithms, automatic winner selection using randomization logic, and detailed user dashboards.

This paper deals with several use-cases for privately querying corpora of documents in both settings where the corpus is public or private with respect to an honest-but-curious infrastructure executing the query. We address these scenarios using Fully Homomorphic Encryption (FHE) hybridized with other techniques such as Symmetric Searchable Encryption (SSE) and Private Information Retrieval (PIR) to achieve acceptable system level performances. The paper also presents the prototypes developed to validate the approach and reports on the performances obtained as well as their capacity to scale.

Smart grid, envisioned as an indispensable power infrastructure, is featured by real-time and two-way communications. However, how to securely retrieve and audit the communicated metering data for validation testing is still challenging for smart grid.The present electric power system structure has lasted for decades; it is still partially proprietary, energy inefficient, physically and virtually (or cyber) insecure, as well as prone to power transmission congestion and consequent failures. Recent efforts in building a smart grid system have focused on addressing the problems of global warming effects, rising energy-hungry demands, and risks of peak loads. One of the major goals of the new system is to effectively regulate energy usage by utilizing the backbone of the prospectively deployed Automatic Meter Reading (AMR), Advanced Meter Infrastructure (AMI), and Demand Response (DR) programs via the advanced distribution automation and dynamic pricing models, Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance.

This paper proposes two closely related asymmetric key (or a public key) schemes for key exchange whose security is based on the notion of ideal secrecy. In the first scheme, the private key consists of two singular matrices, a polar code matrix and a random permutation matrix all over the binary field. The sender transmits addition of two messages over a public channel using the public key of the receiver. The receiver can decrypt individual messages using the private key. An adversary, without the knowledge of the private key, can only compute multiple equiprobable solutions in a space of sufficiently large size related to the dimension of the kernel of the singular matrices. This achieves security in the sense of ideal secrecy. The next scheme extends over general matrices. The two schemes are cryptanalyzed against various attacks.