Although the state-of-the-art neural network model extraction attack in the hard-label setting by Carlini {\em et al.} at EUROCRYPT 2025 has polynomial-time complexity in theory, its dual-point clustering relies on singular value decomposition (SVD) with a time complexity of $\mathcal{O}(n^2 \cdot (d^{(k)})^3)$, resulting in huge runtime in practice. To address this computational bottleneck, this work transforms Carlini {\em et al.}'s geometric-view hard-label attack into an algebraic...

Neural network model extraction has recently emerged as a critical security issue. In 2020, Carlini et al. categorized model extraction into signature extraction and sign extraction. In 2024, Canales-Martínez et al. proposed a polynomial-time sign extraction method. In 2026, Liu et al. achieved the first successful model extraction of 8-layer deep neural networks. However, existing signature extraction methods follow an inefficient compute-first, cluster-later paradigm: they first compute...

The transition to post-quantum cryptography (PQC) is constrained by the limited cryptanalytic history of individual PQC algorithms. Hybrid constructions, which combine several primitives so that breaking the hybrid requires breaking each component, address this concern directly. This paper presents Cobra, a hybrid Key Encapsulation Mechanism (KEM) that integrates FrodoKEM (unstructured LWE), ML-KEM (FIPS 203 module-LWE), HQC (code-based), and a Dummy KEM for agility, and analyses all 15...

Federated Learning (FL) is a collaborative Machine Learning (ML) process where clients locally train an ML model on their private inputs, and send it to a server that aggregates the local model updates to obtain a global model update. FL is widely used in applications where the training data is distributed among several clients, e.g., for next word prediction in Google keyboard (Gboard). Nevertheless, FL faces several challenges concerning privacy and security. 1) Client privacy needs to be...

Neural network model extraction attacks pose a serious threat to the intellectual property of deep learning models. While most prior work focuses on Fully Connected Networks (FCNs), effective extraction of Convolutional Neural Networks (CNNs) remains underexplored, particularly in the hard-label setting. In this work, we propose the first systematic method for the recovery of complete CNN parameters in such conditions. By reformulating convolutional layers as sparse Block Toeplitz with...

This work presents the first third-party cryptanalysis of \textsf{Blink}, a recent tweakable block cipher built on the Three-Hash Framework with a long-key design. Leveraging the idea of Superbox, we develop a lightweight theoretical model capturing the value correlations, weak-key conditions, fixed-key probabilities and the local clustering behaviors inside a \textsf{Blink} Superbox when the value spaces are affine. This model is intended as a concrete, easy-to-follow specialization of...

Many design strategies and differential attacks rely on the so-called hypothesis of stochastic equivalence, which states that the differential behaviour of a cipher for any fixed key can be approximated by its average behaviour over all keys. However, this assumption is known to be invalid in general. For instance, all two-round differential characteristics of AES are plateau, meaning that their probabilities highly depend on the key. While such discrepancies were traditionally expected to...

As an ISO/IEC standard, RIPEMD-160 has been extensively studied for (Semi-Free-Start) collision attacks. A significant breakthrough was achieved at FSE 2024 with the first 41-, 42-, and 43-step SFS collision attacks, which leveraged an automatic search model (EUROCRYPT 2023) and a message modification strategy (FSE 2020). However, these attacks are limited by reliance on heuristic objective functions and suboptimal message modification techniques. This paper enhances the existing framework...

High-throughput technologies (e.g., the microarray) have fostered the rapid growth of gene expression data collection. These biomedical datasets, increasingly distributed among research institutes and hospitals, fuel various machine learning applications such as anomaly detection, prediction or clustering. In particular, unsupervised classification techniques based on biclustering like the Cheng and Church Algorithm (CCA) have proven to adapt particularly well to gene expression data....

Deep learning-based side-channel attack (DLSCA) has become the dominant paradigm for extracting sensitive information from hardware implementations due to its ability to learn discriminative features directly from raw side-channel traces. A common design choice in DLSCA involves embedding traces in Euclidean space, where the underlying geometry supports conventional objectives such as classification or contrastive learning. However, Euclidean space is fundamentally limited in capturing the...

In the last years, Deep Learning algorithms have been browsed and applied to Side-Channel Analysis in order to enhance attack’s performances. In some cases, the proposals came without an indepth analysis allowing to understand the tool, its applicability scenarios, its limitations and the advantages it brings with respect to classical statistical tools. As an example, a study presented at CHES 2021 proposed a corrective iterative framework to perform an unsupervised attack which achieves a...

Side-channel analysis remains a critical threat to public-key cryptographic implementations. Simple Power Analysis (SPA) techniques can extract secret keys from a single power trace, often using clustering-based classification methods. However, traces captured in real-world environments often suffer from misalignment and variable trace lengths due to unstable clocks and random delays. As a result, clustering methods are required to use alignment methods that may alter the original...

Lightweight block ciphers such as PIPO and FLY are designed to operate efficiently and securely in constrained environments. While the differential attack on PIPO-64-128 has already been studied by the designers, no concrete differential attack had been conducted for PIPO-64-256 and FLY. Motivated by this gap, we revisit the security of PIPO against differential attacks and generalize the analysis framework to make it applicable to structurally related ciphers. Based on this generalized...

In recent years, the integration of deep learning with differential cryptanalysis has led to differential neural cryptanalysis, enabling efficient data-driven security evaluation of modern cryptographic algorithms. Compared to traditional differential cryptanalysis, differential neural cryptanalysis enhances the efficiency and automation of the analysis by training neural networks to automatically extract statistical features from ciphertext pairs. As research advances, neural distinguisher...

Masking typically relies on fresh online randomness to prevent higher-order side-channel leakage, which is costly to generate on constrained devices. We study the extreme setting of deterministic masking, where a circuit can only recycle the entropy already present in the input sharing, and we address the key obstacle of building nonlinear gadgets that are both higher-order secure and uniform. Our main tool is a share-clustering framework inspired by finite affine geometry, which for...

In block ciphers, the attacker should not be able to distinguish a block cipher from a random permutation; therefore the existence of a distinguisher is important. Cryptanalysis of the reduced-round variants of block ciphers is also important in cryptographic design. AES is the most widely used block cipher, and currently, the best-known distinguisher for 5-round AES has a data and time complexity of $2^{29.95}$ with a success probability of 55\%. In this paper, we propose the massive...

Physical side-channel analysis (SCA) operates on the foundational assumption of access to known plaintext or ciphertext. However, this assumption can be easily invalidated in various scenarios, ranging from common encryption modes like Cipher Block Chaining (CBC) to complex hardware implementations, where such data may be inaccessible. Blind SCA addresses this challenge by operating without the knowledge of plaintext or ciphertext. Unfortunately, prior such approaches have shown limited...

Federated Learning, as a multi-party machine learning paradigm, has garnered significant attention, but model updates may still leak sensitive information. Secure aggregation protocols are considered an effective solution for privacy protection in Federated Learning. However, in large-scale federated learning systems, designing efficient and practical secure aggregation remains a critical challenge. Moreover, while secure aggregation effectively conceals model updates, it unintentionally...

Side-channel analysis is a powerful technique to extract secret data from cryptographic devices. However, this task heavily relies on experts and specialized tools, particularly in the case of simple power analysis (SPA). Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in assisting users with complex tasks, yet their potential for fully automated SPA remains unexplored. In this paper, we propose a novel prompt framework specifically designed for SPA tasks,...

Clustering is a crucial unsupervised learning method extensively used in the field of data analysis. For analyzing big data, outsourced computation is an effective solution but privacy concerns arise when involving sensitive information. Fully homomorphic encryption (FHE) enables computations on encrypted data, making it ideal for such scenarios. However, existing privacy-preserving clustering based on FHE are often constrained by the high computational overhead incurred from FHE, typically...

This paper deals with reducing the secret key computation time of small scale variants of the AES cipher using algebraic cryptanalysis, which is accelerated by data mining methods. This work is based on the known plaintext attack and aims to speed up the calculation of the secret key by processing the polynomial equations extracted from plaintext-ciphertext pairs. Specifically, we propose to transform the overdefined system of polynomial equations over GF(2) into a new system so that the...

Generative pre-trained transformers (GPT's) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. Notably, these technologies have also been extended to computer programming languages with great success. However, GPT model outputs in general are stochastic and not always correct. For programming languages, the exact specification of the computer code, syntactically and algorithmically, is strictly required in order to...

Federated Learning (FL) accomplishes collaborative model training without the need to share local training data. However, existing FL aggregation approaches suffer from inefficiency, privacy vulnerabilities, and neglect of poisoning attacks, severely impacting the overall performance and reliability of model training. In order to address these challenges, we propose SuperFL, an efficient two-server aggregation scheme that is both privacy preserving and secure against poisoning attacks. The...

Decentralized Finance, mushrooming in permissionless blockchains, has attracted a recent surge in popularity. Due to the transparency of permissionless blockchains, opportunistic traders can compete to earn revenue by extracting Miner Extractable Value (MEV), which undermines both the consensus security and efficiency of blockchain systems. The Flashbots bundle mechanism further aggravates the MEV competition because it empowers opportunistic traders with the capability of designing more...

Power analysis of public-key algorithms is a well-known approach in the community of side-channel analysis. We usually classify operations based on the differences in power traces produced by different basic operations (such as modular exponentiation) to recover secret information like private keys. The more accurate the segmentation of power traces, the higher the efficiency of their classification. There exist two commonly used methods: one is equidistant segmentation, which requires a...

We propose a new compiler framework that automates code generation over multiple fully homomorphic encryption (FHE) schemes. While it was recently shown that algorithms combining multiple FHE schemes (e.g., CKKS and TFHE) achieve high execution efficiency and task utility at the same time, developing fast cross-scheme FHE algorithms for real-world applications generally require heavy hand-tuned optimizations by cryptographic experts, resulting in either high usability costs or low...

The most crucial but time-consuming task for differential cryptanalysis is to find a differential with a high probability. To tackle this task, we propose a new SAT-based automatic search framework to efficiently figure out a differential with the highest probability under a specified condition. As the previous SAT methods (e.g., the Sun et al’s method proposed at ToSC 2021(1)) focused on accelerating the search for an optimal single differential characteristic, these are not optimized for...

As low-latency designs tend to have a small number of rounds to decrease latency, the differential-type cryptanalysis can become a significant threat to them. In particular, since a multiple-branch-based design, such as Orthros can have the strong clustering effect on differential attacks due to its large internal state, it is crucial to investigate the impact of the clustering effect in such a design. In this paper, we present a new SAT-based automatic search method for evaluating the...

With the advent of secure function evaluation (SFE), distrustful parties can jointly compute on their private inputs without disclosing anything besides the results. Yao’s garbled circuit protocol has become an integral part of secure computation thanks to considerable efforts made to make it feasible, practical, and more efficient. These efforts have resulted in multiple optimizations on this primitive to enhance its performance by orders of magnitude over the last years. The advancement in...

Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed...

We consider a foundational unsupervised learning task of k-means data clustering, in a federated learning (FL) setting consisting of a central server and many distributed clients. We develop SecFC, which is a secure federated clustering algorithm that simultaneously achieves 1) universal performance: no performance loss compared with clustering over central- ized data, regardless of data distribution across clients; 2) data privacy: each client’s private data and the cluster centers are not...

In this study, we accelerate Matsui's search algorithm to search for the best differential and linear trails of AES-like ciphers. Our acceleration points are twofold. The first exploits the structure and branch number of an AES-like round function to apply strict pruning conditions to Matsui's search algorithm. The second employs permutation characteristics in trail search to reduce the inputs that need to be analyzed. We demonstrate the optimization of the search algorithm by obtaining the...

Inverse sqrt and sqrt function have numerous applications in linear algebra and machine learning such as vector normalisation, eigenvalue computation, dimensionality reduction, clustering, etc. This paper presents a method to approximate and securely perform the inverse sqrt function using CKKS homomorphic encryption scheme. Since the CKKS homomorphic scheme allows only computation of polynomial functions, we propose a method to approximate the inverse sqrt function polynomially. In the end,...

In this paper, the recommended implementation of the post-quantum key exchange SIKE for Cortex-M4 is attacked through power analysis with a single trace by clustering with the $k$-means algorithm the power samples of all the invocations of the elliptic curve point swapping function in the constant-time coordinate-randomized three point ladder. Because each sample depends on whether two consecutive bits of the private key are the same or not, a successful clustering (with $k=2$) leads to the...

Simon and Simeck are two lightweight block ciphers with a simple round function using only word rotations and a bit-wise AND operation. Previous work has shown a strong clustering effect for differential and linear cryptanalysis, due to the existence of many trails with the same inputs and outputs. In this paper, we explore this clustering effect by exhibiting a class of high probability differential and linear trails where the active bits stay in a fixed window of $w$ bits. Instead of...

We propose a tool for automated truncation of differential trails in ciphers using modular addition, bitwise rotation, and XOR (ARX). The tool takes as input a differential trail and produces as output a set of truncated differential trails. The set represents all possible truncations of the input trail according to certain predefined rules. A linear-time algorithm for the exact computation of the differential probability of a truncated trail that follows the truncation rules is proposed. We...

This study proposes a chosen-ciphertext side-channel attack against a lattice-based key encapsulation mechanism (KEM), the third-round candidate of the national institute of standards and technology (NIST) standardization project. Unlike existing attacks that target operations such as inverse NTT and message encoding/decoding, we target Barrett Reduction in the decapsulation phase of CRYSTALS-KYBER to obtain a secret key. We show that a sensitive variable-dependent leakage of Barrett...

In many machine learning applications, training data consists of sensitive information from multiple sources. Privacy-preserving machine learning using secure computation enables multiple parties to compute on their joint data without disclosing their inputs to each other. In this work, we focus on clustering, an unsupervised machine learning technique that partitions data into groups. Previous works on privacy-preserving clustering often leak information and focus on the k-means algorithm,...

Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient...

CryptoNote blockchains like Monero represent the largest public deployments of linkable ring signatures. Beginning with the work of Kumar et al. (ESORICS 2017) and Möser et al. (PoPETs 2018), several techniques have been proposed to trace CryptoNote transactions, i.e. identify the actual signing key, by using the transaction history. Yu et al. (FC 2019) introduced the closed set attack for undeniable traceability and proved that it is optimal by showing that it has the same performance as...

Clustering is an unsupervised machine learning technique that outputs clusters containing similar data items. In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis. When (multiple) data owners collaborate or outsource the computation, privacy concerns arise. To address this problem, we design, implement, and evaluate the first practical and fully private density-based clustering scheme based on secure...

Designing a block cipher or cryptographic permutation can be approached in many different ways. One such approach, popularized by AES, consists in grouping the bits along the S-box boundaries, e.g., in bytes, and in consistently processing them in these groups. This aligned approach leads to hierarchical structures like superboxes that make it possible to reason about the differential and linear propagation properties using combinatorial arguments. In contrast, an unaligned approach avoids...

Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others. Despite its benefits, FL is vulnerable to so-called backdoor attacks, in which an adversary injects manipulated model updates into the federated model aggregation process so that the resulting model will provide targeted false predictions for specific adversary-chosen inputs. Proposed...

Many web-based and mobile applications and services allow users to indicate their preferences regarding whether and how their personal information can be used or reused by the application itself, by the service provider, and/or by third parties. The number of possible configurations that constitute a user's preference profile can be overwhelming to a typical user. This report describes a practical, privacy-preserving technique for reducing the burden users face when specifying their...

Homomorphic encryption (HE) is considered as one of the most important primitives for privacy-preserving applications. However, an efficient approach to evaluate both polynomial and non-polynomial functions on encrypted data is still absent, which hinders the deployment of HE to real-life applications. To address this issue, we propose a practical framework PEGASUS. PEGASUS can efficiently switch back and forth between a packed CKKS ciphertext and FHEW ciphertexts without...

We consider interactive proofs for social graphs, where the verifier has only oracle access to the graph and can query for the $i^{th}$ neighbor of a vertex $v$, given $i$ and $v$. In this model, we construct a doubly-efficient public-coin two-message interactive protocol for estimating the size of the graph to within a multiplicative factor $\epsilon>0$. The verifier performs $\tilde{O}(1/\epsilon^2 \cdot \tau_{mix} \cdot \Delta)$ queries to the graph, where $\tau_{mix}$ is the mixing time...

In this paper, we reevaluate the security of GIFT against differential cryptanalysis under both single-key scenario and related-key scenario. Firstly, we apply Matsui's algorithm to search related-key differential trails of GIFT. We add three constraints to limit the search space and search the optimal related-key differential trails on the limited search space. We obtain related-key differential trails of GIFT-64/128 for up to 15/14 rounds, which are the best results on related-key...

To mitigate side-channel attacks, real-world implementations of public-key cryptosystems adopt state-of-the-art countermeasures based on randomization of the private or ephemeral keys. Usually, for each private key operation, a "scalar blinding" is performed using 32 or 64 randomly generated bits. Nevertheless, horizontal attacks based on a single trace still pose serious threats to protected ECC or RSA implementations. If the secrets learned through a single-trace attack contain too many...

In this paper, we propose that countermeasures against instruction-related timing attack would be vulnerable to single-trace attacks, which are presented at ISPEC 2017 and CHES 2019. The countermeasures use determiner to make operations, which leak timing side-channel information, perform in a constant-time. Since determiner is divided into two groups according to secret credentials, it is possible to recover secret credentials by clustering determiner into two groups.

Differential cryptanalysis of block ciphers requires the identification of differential characteristics with high probability. For block ciphers with large block sizes and number of rounds, identifying these characteristics is computationally intensive. The branch-and-bound algorithm was proposed by Matsui to automate this task. Since then, numerous improvements were made to the branch-and-bound algorithm by bounding the number of active s-boxes, incorporating a meet-in-the-middle approach,...

Automatic search methods have been widely used for cryptanalysis of block ciphers, especially for the most classic cryptanalysis methods -- differential and linear cryptanalysis. However, the automatic search methods, no matter based on MILP, SMT/SAT or CP techniques, can be inefficient when the search space is too large. In this paper, we improve Matsui's branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails by...

Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacy-preserving context. Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the...

With the rapid development of cloud computing, searchable encryption for multiple data owners model (multi-owner model) draws much attention as it enables data users to perform searches on encrypted cloud data outsourced by multiple data owners. However, there are still some issues yet to be solved nowadays, such as precise query, fast query, dimension disaster and flexible system dynamic maintenance. To target these issues, this paper proposes a secure and efficient multi-keyword ranked...

Recently with the advent of technology, a lot of data are stored and mined in cloud servers. Since most of the data contain potential private information, it has become necessary to preserve the privacy in data mining. In this paper, we propose a protocol for collaboratively performing the K-means clustering algorithm on the data distributed among multiple data owners, while protecting the sensitive private data. We employ two service providers in our scenario, namely a main service provider...

Cluster analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issues including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving cluster analysis based on homomorphic encryption~(HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a...

A chosen-prefix collision attack is a stronger variant of a collision attack, where an arbitrary pair of challenge prefixes are turned into a collision. Chosen-prefix collisions are usually significantly harder to produce than (identical-prefix) collisions, but the practical impact of such an attack is much larger. While many cryptographic constructions rely on collision-resistance for their security proofs, collision attacks are hard to turn into a break of concrete protocols, because the...

The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and receives the response in which case the query and response are revealed to the service provider. Such data disclosures are unacceptable in several scenarios due to the sensitivity of data and/or privacy laws. In this paper, we introduce SANNS, a...

In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model. In this work, we take the first step into the realm of unsupervised learning, which is an important area in Machine Learning and has many real-world applications, by addressing...

Side-channel attacks are a threat to cryptographic algorithms running on embedded devices. Public-key cryptosystems, including elliptic curve cryptography (ECC), are particularly vulnerable because their private keys are usually long-term. Well known countermeasures like regularity, projective coordinates and scalar randomization, among others, are used to harden implementations against common side-channel attacks like DPA. Horizontal clustering attacks can theoretically overcome these...

The TWEAKEY/STK construction is an increasingly popular approach for designing tweakable block ciphers that notably uses a linear tweakey schedule. Several recent attacks have analyzed the implications of this approach for differential cryptanalysis and other attacks that can take advantage of related tweakeys. We generalize the clustering approach of a recent differential attack on the tweakable block cipher MANTIS-5 and describe a tool for efficiently finding and evaluating such...

Privacy preserving computation is gaining importance. Along with secure computation guarantees, it is essential to hide information leakage through access patterns. Input-oblivious execution is a security property that is crucial to guarantee complete privacy preserving computation. In this work, we present an algorithm-specific approach to achieve input-oblivious execution. We call this class of algorithms PermuteRam. PermuteRam algorithms satisfy a specific patterns in their execution...

In distributed storage systems, documents are shared among multiple Cloud providers and stored within their respective storage servers. In social secret sharing-based distributed storage systems, shares of the documents are allocated according to the trustworthiness of the storage servers. This paper proposes a trust mechanism using machine learning techniques to compute evidence-based trust values. Our mechanism mitigates the effect of colluding storage servers. More precisely, it becomes...

In this paper we propose MIE, a Multimodal Indexable Encryption framework that for the first time allows mobile applications to securely outsource the storage and search of their multimodal data (i.e. data containing multiple media formats) to public clouds with privacy guarantees. MIE is designed as a distributed framework architecture, leveraging on shared cloud repositories that can be accessed simultaneously by multiple users. At its core MIE relies on Distance Preserving Encodings...

Side-channel attacks put the security of the implementations of cryptographic algorithms under threat. Secret information can be recovered by analyzing the physical measurements acquired during the computations and using key recovery distinguishing functions to guess the best candidate. Several generic and model based distinguishers have been proposed in the literature. In this work we describe two contributions that lead to better performance of side-channel attacks in challenging...

WPA2-Personal is widely used to protect Wi-Fi networks against illicit access. While attackers typically use GPUs to speed up the discovery of weak network passwords, attacking random passwords is considered to quickly become infeasible with increasing password length. Professional attackers may thus turn to commercial high-end FPGA-based cluster solutions to significantly increase the speed of those attacks. Well known manufacturers such as Elcomsoft have succeeded in creating...

Establishing friendship relationships on Facebook often entails information sharing which is based on the social trust and implicit contract between users and their friends. In this context, Facebook offers applications (Apps) developed by third-party application providers (AppPs), which may grant access to users' personal data via Apps installed by their friends. Such access takes place outside the circle of social trust with the user not being aware whether a friend has installed an App...

TWINE is a recent lightweight block cipher based on a Feistel structure. We first present two new attacks on TWINE-128 reduced to 25 rounds that have a slightly higher overall complexity than the 25-round attack presented by Wang and Wu at ACISP 2014, but a lower data complexity. Then, we introduce alternative representations of both the round function of this block cipher and of a sequence of 4 rounds. LBlock, another lightweight block cipher, turns out to exhibit the same behaviour. Then,...

Security is the biggest concern in Wireless Sensor Networks (WSNs) especially for the ones which are deployed for military applications and monitoring. They are prone to various attacks which degrades the network performance very rapidly. Sometimes multiple attacks are launched in the network using hybrid anomaly. In this situation it is very difficult to find out which kind of anomaly is activated. In this paper, we have proposed a hybrid anomaly detection technique with the application of...

The paper is about methodology to detect and demonstrate impossible differentials in a block cipher. We were inspired by the shrinking technique proposed by Biham et al. in 1999 which recovered properties of scalable block cipher structures from numerical search on scaled down variants. Attempt to bind all concepts and techniques of impossible differentials together reveals a view of the search for impossible differentials that can benefit from the computational power of a computer. We...

Most implementations of public key cryptography employ exponentiation algorithms. Side-channel attacks on secret exponents are typically bound to the leakage of single executions due to cryptographic protocols or side-channel countermeasures such as blinding. We propose for the first time, to use a well-established class of algorithms, i.e. unsupervised cluster classification algorithms such as the k-means algorithm to attack cryptographic exponentiations and recover secret exponents without...

Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy guarantees of Bitcoin in the setting where Bitcoin is used as a primary currency for the daily transactions of individuals. More specifically, we evaluate the privacy that is provided by Bitcoin...

Side channel attacks take advantage of the information leakage in a cryptographic device. A template attack is a family of side channel attacks which is reputed to be extremely effective. This kind of attacks supposes that the attacker can fully control a cryptographic device before attacking a similar one. In this paper, we propose a method based on a semi-supervised learning strategy to relax this assumption. The effectiveness of our proposal is confirmed by software simulations as well as...

Trust Based Collaborative Search is an interactive metasearch engine, presenting the user with clusters of results, based not only on the similarity of content, but also on the similarity of the recommending agents. The theory presented here is broad enough to cover search, browsing, recommendations, demographic profiling, and consumer targeting. We use the term search as an example. We developed a novel general trust theory. In this context, as a special case, we equate trust between...

We developed three new theoretical insights into the art of hierarchical clustering in the context of web-search. A no- table example where these results may be useful is Trust Based Collaborative Search, where an active user consults agents that in the past performed a similar search. We pro- ceed with this as an example throughout the paper, even though the results are more broadly applicable. The …rst result is that under plausible conditions, trust converges to the extremes,...

The k-Means Clustering problem is one of the most-explored problems in data mining to date. With the advent of protocols that have proven to be successful in performing single database clustering, the focus has changed in recent years to the question of how to extend the single database protocols to a multiple database setting. To date there have been numerous attempts to create specific multiparty k-means clustering protocols that protect the privacy of each database, but according to the...

The input to the Graph Clustering Problem consists of a sequence of integers $m_1,...,m_t$ and a sequence of $\sum_{i=1}^{t}m_i$ graphs. The question is whether the equivalence classes, under the graph isomorphism relation, of the input graphs have sizes which match the input sequence of integers. In this note we show that this problem has a (perfect) zero-knowledge interactive proof system. This result improves over <a href="http:../1996/96-14.html">record 96-14</a>, where a parametrized...

The Graph Clustering Problem is parameterized by a sequence of positive integers, $m_1,...,m_t$. The input is a sequence of $\sum_{i=1}^{t}m_i$ graphs, and the question is whether the equivalence classes under the graph isomorphism relation have sizes which match the sequence of parameters. In this note we show that this problem has a (perfect) zero-knowledge interactive proof system.