A Secure Modified ID-Based Undeniable Signature Scheme

Verifiable Pairing and its Applications. In Chae Hoon Lim and Moti Yung, editors, Information Security Applications: 5th International Workshop, WISA 2004, Jeju Island, Korea, August 23-25, 2004, Revised Selected Papers, volume 3325 of Lecture Notes in Computer Science, pp. 170-187. (http://www.springerlink.com/index/C4QB7C13NL0EY5VN) which contains an improved and generalized result of this paper

Efficient enhanced keyword search for encrypted document in cloud

A sensitive public-key searchable encryption system in the prime-order groups, which lets keyword search policies to be uttered in conjunctive, disjunctive or any monotonic Boolean formulas and realizes momentous act enhancement over existing schemes. We legally express its sanctuary, and verify that it is selectively sheltered in the standard model. Correspondingly, we instrument the wished-for outline using a hasty prototyping tool so-called Charm and conduct more than a few experiments to estimate it show. The results determine that our scheme is plentiful more proficient than the ones assembled over the composite-order groups. Keyword research is one of the most imperative, valuable, and high return activities in the search marketing field. Position for the right keywords can make or interruption your website

Towards Easy Key Enumeration

Key enumeration solutions are post-processing schemes for the output sequences of side channel distinguishers, the application of which are prevented by very large key candidate space and computation power requirements. The attacker may spend several days or months to enumerate a huge key space (e.g. $2^{40}$). In this paper, we aim at pre-processing and reducing the key candidate space by deleting impossible key candidates before enumeration. A new distinguisher named Group Collision Attack (GCA) is given. Moreover, we introduce key verification into key recovery and a new divide and conquer strategy named Key Grouping Enumeration (KGE) is proposed. KGE divides the huge key space into several groups and uses GCA to delete impossible key combinations and output possible ones in each group. KGE then recombines the remaining key candidates in each group using verification. The number of remaining key candidates becomes much smaller through these two impossible key candidate deletion steps with a small amount of computation. Thus, the attacker can use KGE as a pre-processing tool of key enumeration and enumerate the key more easily and fast in a much smaller candidate space

Manifold Learning Towards Masking Implementations: A First Study

Linear dimensionality reduction plays a very important role in side channel attacks, but it is helpless when meeting the non-linear leakage of masking implementations. Increasing the order of masking makes the attack complexity grow exponentially, which makes the research of nonlinear dimensionality reduction very meaningful. However, the related work is seldom studied. A kernel function was firstly introduced into Kernel Discriminant Analysis (KDA) in CARDIS 2016 to realize nonlinear dimensionality reduction. This is a milestone for attacking masked implementations. However, KDA is supervised and noise-sensitive. Moreover, several parameters and a specialized kernel function are needed to be set and customized. Different kernel functions, parameters and the training results, have great influence on the attack efficiency. In this paper, the high dimensional non-linear leakage of masking implementation is considered as high dimensional manifold, and manifold learning is firstly introduced into side channel attacks to realize nonlinear dimensionality reduction. Several classical and practical manifold learning solutions such as ISOMAP, Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE) are given. The experiments are performed on the simulated unprotected, first-order and second-order masking implementations. Compared with supervised KDA, manifold learning schemes introduced here are unsupervised and fewer parameters need to be set. This makes manifold learning based nonlinear dimensionality reduction very simple and efficient for attacking masked implementations

Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning

International audienceThe ability to efficiently detect the software protections used is at a prime to facilitate the selection and application of adequate deob-fuscation techniques. We present a novel approach that combines semantic reasoning techniques with ensemble learning classification for the purpose of providing a static detection framework for obfuscation transformations. By contrast to existing work, we provide a methodology that can detect multiple layers of obfuscation, without depending on knowledge of the underlying functionality of the training-set used. We also extend our work to detect constructions of obfuscation transformations, thus providing a fine-grained methodology. To that end, we provide several studies for the best practices of the use of machine learning techniques for a scalable and efficient model. According to our experimental results and evaluations on obfuscators such as Tigress and OLLVM, our models have up to 91% accuracy on state-of-the-art obfuscation transformations. Our overall accuracies for their constructions are up to 100%

Information Entropy Based Leakage Certification

Side-channel attacks and evaluations typically utilize leakage models to extract sensitive information from measurements of cryptographic implementations. Efforts to establish a true leakage model is still an active area of research since Kocher proposed Differential Power Analysis (DPA) in 1999. Leakage certification plays an important role in this aspect to address the following question: how good is my leakage model? . However, existing leakage certification methods still need to tolerate assumption error and estimation error of unknown leakage models. There are many probability density distributions satisfying given moment constraints. As such, finding the most unbiased and most reasonable model still remains an unresolved problem. In this paper, we address a more fundamental question: what\u27s the true leakage model of a chip? . In particular, we propose Maximum Entropy Distribution (MED) to estimate the leakage model as MED is the most unbiased, objective and theoretically the most reasonable probability density distribution conditioned upon the available information. MED can theoretically use information on arbitrary higher-order moments to infinitely approximate the true leakage model. It well compensates the theory vacancy of model profiling and evaluation. Experimental results demonstrate the superiority of our proposed method for approximating the leakage model using MED estimation

CoTree: Push the Limits of Conquerable Space in Collision-Optimized Side-Channel Attacks

By introducing collision information into side-channel distinguishers, the existing collision-optimized attacks exploit collision detection algorithm to transform the original candidate space under consideration into a significantly smaller collision chain space, thus achieving more efficient key recovery. However, collision information is detected very repeatedly since collision chains are created from the same sub-chains, i.e., with the same candidates on their first several sub-keys. This aggravates when exploiting more collision information. The existing collision detection algorithms try to alleviate this, but the problem is still very serious. In this paper, we propose a highly-efficient detection algorithm named Collision Tree (CoTree) for collision-optimized attacks. CoTree exploits tree structure to store the chains creating from the same sub-chain on the same branch. It then exploits a top-down tree building procedure and traverses each node only once when detecting their collisions with a candidate of the sub-key currently under consideration. Finally, it launches a bottom-up branch removal procedure to remove the chains unsatisfying the collision conditions from the tree after traversing all candidates (within given threshold) of this sub-key, thus avoiding the traversal of the branches satisfying the collision condition. These strategies make our CoTree significantly alleviate the repetitive collision detection, and our experiments verify that it significantly outperforms the existing works

Single-trace clustering power analysis of the point-swapping procedure in the three point ladder of Cortex-M4 SIKE

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 recovery of the entire private key. The attack is naturally improved with better strategies, such as clustering the samples in the frequency domain or processing the traces with a wavelet transform, using a simpler clustering algorithm based on thresholding, and using metrics to prioritize certain keys for key validation. The attack and the proposed improvements were experimentally verified using the ChipWhisperer framework. Splitting the swapping mask into multiple shares is suggested as an effective countermeasure