A Statistics-based Fundamental Model for Side-channel Attack Analysis

ide-channel attacks (SCAs) exploit leakage from the physical implementation of cryptographic algorithms to recover the otherwise secret information. In the last decade, popular SCAs like differential power analysis (DPA) and correlation power analysis (CPA) have been invented and demonstrated to be realistic threats to many critical embedded systems. However, there is still no sound and provable theoretical model that illustrates precisely what the success of these attacks depends on and how. Based on the maximum likelihood estimation (MLE) theory, this paper proposes a general statistical model for side-channel attack analysis that takes characteristics of both the physical implementation and cryptographic algorithm into consideration. The model establishes analytical relations between the success rate of attacks and the cryptographic system. For power analysis attacks, the side-channel characteristic of the physical implementation is modeled as signal-to-noise ratio (SNR), which is the ratio between the single-bit unit power consumption and the standard deviation of power distribution. The side-channel property of the cryptographic algorithm is extracted by a novel algorithmic confusion analysis. Experimental results of DPA and CPA on both DES and AES verify this model with high accuracy and demonstrate effectiveness of the algorithmic confusion analysis and SNR extraction. We expect the model to be extendable to other SCAs, like timing attacks, and would provide valuable guidelines for truly SCA-resilient system design and implementation

A Statistical Model for Higher Order DPA on Masked Devices

A popular effective countermeasure to protect block cipher implementations against differential power analysis (DPA) attacks is to mask the internal operations of the cryptographic algorithm with random numbers. While the masking technique resists against first-order (univariate) DPA attacks, higher-order (multivariate) attacks were able to break masked devices. In this paper, we formulate a statistical model for higher-order DPA attack. We derive an analytic success rate formula that distinctively shows the effects of algorithmic confusion property, signal-noise-ratio (SNR), and masking on leakage of masked devices. It further provides a formal proof for the centered product combination function being optimal for higher-order attacks in very noisy scenarios. We believe that the statistical model fully reveals how the higher-order attack works around masking, and would offer good insights for embedded system designers to implement masking techniques

Slave to the Algorithm? Why a \u27Right to an Explanation\u27 Is Probably Not the Remedy You Are Looking For

Algorithms, particularly machine learning (ML) algorithms, are increasingly important to individuals’ lives, but have caused a range of concerns revolving mainly around unfairness, discrimination and opacity. Transparency in the form of a “right to an explanation” has emerged as a compellingly attractive remedy since it intuitively promises to open the algorithmic “black box” to promote challenge, redress, and hopefully heightened accountability. Amidst the general furore over algorithmic bias we describe, any remedy in a storm has looked attractive. However, we argue that a right to an explanation in the EU General Data Protection Regulation (GDPR) is unlikely to present a complete remedy to algorithmic harms, particularly in some of the core “algorithmic war stories” that have shaped recent attitudes in this domain. Firstly, the law is restrictive, unclear, or even paradoxical concerning when any explanation-related right can be triggered. Secondly, even navigating this, the legal conception of explanations as “meaningful information about the logic of processing” may not be provided by the kind of ML “explanations” computer scientists have developed, partially in response. ML explanations are restricted both by the type of explanation sought, the dimensionality of the domain and the type of user seeking an explanation. However, “subject-centric explanations (SCEs) focussing on particular regions of a model around a query show promise for interactive exploration, as do explanation systems based on learning a model from outside rather than taking it apart (pedagogical versus decompositional explanations) in dodging developers\u27 worries of intellectual property or trade secrets disclosure. Based on our analysis, we fear that the search for a “right to an explanation” in the GDPR may be at best distracting, and at worst nurture a new kind of “transparency fallacy.” But all is not lost. We argue that other parts of the GDPR related (i) to the right to erasure ( right to be forgotten ) and the right to data portability; and (ii) to privacy by design, Data Protection Impact Assessments and certification and privacy seals, may have the seeds we can use to make algorithms more responsible, explicable, and human-centered

A Unified Metric for Quantifying Information Leakage of Cryptographic Devices under Power Analysis Attacks

To design effective countermeasures for cryptosystems against side-channel power analysis attacks, the evaluation of the system leakage has to be lightweight and often times at the early stage like on cryptographic algorithm or source code. When real implementations and power leakage measurements are not available, security evaluation has to be through metrics for the information leakage of algorithms. In this work, we propose such a general and unified metric, information leakage amount - ILA. ILA has several distinct advantages over existing metrics. It unifies the measure of information leakage to various attacks: first-order and higher-order DPA and CPA attacks. It works on algorithms with no mask protection or perfect/imperfect masking countermeasure.It is explicitly connected to the success rates of attacks, the ultimate security metric on physical implementations. Therefore, we believe ILA is an accurate indicator of the side-channel security level of the physical system, and can be used during the countermeasure design stage effectively and efficiently for choosing the best countermeasure

Side Channel Leakage Analysis - Detection, Exploitation and Quantification

Nearly twenty years ago the discovery of side channel attacks has warned the world that security is more than just a mathematical problem. Serious considerations need to be placed on the implementation and its physical media. Nowadays the ever-growing ubiquitous computing calls for in-pace development of security solutions. Although the physical security has attracted increasing public attention, side channel security remains as a problem that is far from being completely solved. An important problem is how much expertise is required by a side channel adversary. The essential interest is to explore whether detailed knowledge about implementation and leakage model are indispensable for a successful side channel attack. If such knowledge is not a prerequisite, attacks can be mounted by even inexperienced adversaries. Hence the threat from physical observables may be underestimated. Another urgent problem is how to secure a cryptographic system in the exposure of unavoidable leakage. Although many countermeasures have been developed, their effectiveness pends empirical verification and the side channel security needs to be evaluated systematically. The research in this dissertation focuses on two topics, leakage-model independent side channel analysis and security evaluation, which are described from three perspectives: leakage detection, exploitation and quantification. To free side channel analysis from the complicated procedure of leakage modeling, an observation to observation comparison approach is proposed. Several attacks presented in this work follow this approach. They exhibit efficient leakage detection and exploitation under various leakage models and implementations. More importantly, this achievement no longer relies on or even requires precise leakage modeling. For the security evaluation, a weak maximum likelihood approach is proposed. It provides a quantification of the loss of full key security due to the presence of side channel leakage. A constructive algorithm is developed following this approach. The algorithm can be used by security lab to measure the leakage resilience. It can also be used by a side channel adversary to determine whether limited side channel information suffices the full key recovery at affordable expense

One for All, All for One: A Unified Evaluation Framework for Univariate DPA Attacks

Success Rate (SR) is empirically and theoretically a common metric for evaluating the performance of side-channel attacks. Intuitive expressions of success rate are desirable since they reveal and explain the functional dependence on relevant parameters, such as number of measurements and Signal-to-Noise Ratio (SNR), in a straightforward manner. Meanwhile, existing works more or less expose unsolved fundamental problems, such as strong leakage assumption, difficulty in interpretation of principle, inaccurate evaluation, and inconsideration of high-order SR. In this paper, we first provide an intuitive framework that statistical tests embedded in different univariate DPA attacks are unified as analyzing and comparing visualized vectors in a Euclidean space by using different easy-to-understand metrics. Then, we establish a unified framework to abstract and convert the security evaluations to the problem of finding a boundary in the Euclidean space. With expressions of the boundary, judging whether a DPA attack succeeds in sense of $o^{th}$-order becomes fairly efficient and intuitive, and the corresponding SR can be calculated theoretically by integral. Finally, we propose an algorithm that is capable of estimating arbitrary order of SR effectively. Our experimental results verify the theory and highlight the superiority. We believe our research raises many new perspectives for comparing and evaluating side-channel attacks, countermeasures and implementations