1,361 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    Revisiting Mutual Information Analysis: Multidimensionality, Neural Estimation and Optimality Proofs

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    Recent works showed how Mutual Information Neural Estimation (MINE) could be applied to side-channel analysis in order to evaluate the amount of leakage of an electronic device. One of the main advantages of MINE over classical estimation techniques is to enable the computation between high dimensional traces and a secret, which is relevant for leakage assessment. However, optimally exploiting this information in an attack context in order to retrieve a secret remains a non-trivial task especially when a profiling phase of the target is not allowed. Within this context, the purpose of this paper is to address this problem based on a simple idea: there are multiple leakage sources in side-channel traces and optimal attacks should necessarily exploit most/all of them. To this aim, a new mathematical framework, designed to bridge classical Mutual Information Analysis (MIA) and the multidimensional aspect of neural-based estimators, is proposed. One of the goals is to provide rigorous proofs consolidating the mathematical basis behind MIA, thus alleviating inconsistencies found in the state of the art. This framework allows to derive a new attack called Neural Estimated Mutual Information Analysis (NEMIA). To the best of our knowledge, it is the first unsupervised attack able to benefit from both the power of deep learning techniques and the valuable theoretical properties of MI. Simulations and experiments show that NEMIA outperforms classical and more recent deep learning based unsupervised side-channel attacks, especially in low-information contexts

    LOL: A Highly Flexible Framework for Designing Stream Ciphers

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    In this paper, we propose LOL, a general framework for designing blockwise stream ciphers, to achieve ultrafast software implementations for the ubiquitous virtual networks in 5G/6G environments and high-security level for post-quantum cryptography. The LOL framework is structurally strong, and all its components as well as the LOL framework itself enjoy high flexibility with various extensions. Following the LOL framework, we propose new stream cipher designs named LOL-MINI and LOL-DOUBLE with the support of the AES-NI and SIMD instructions: the former applies the basic LOL single mode while the latter uses the extended parallel-dual mode. Both LOL-MINI and LOL-DOUBLE support 256-bit key length and, according to our thorough evaluations, have 256-bit security margins against all existing cryptanalysis methods including differential, linear, integral, etc. The software performances of LOL-MINI and LOL-DOUBLE can reach 89 Gbps and 135 Gbps. In addition to pure encryptions, the LOL-MINI and LOL-DOUBLE stream ciphers can also be applied in a stream-cipher-then-MAC strategy to make an AEAD scheme

    Not optimal but efficient: a distinguisher based on the Kruskal-Wallis test

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    Research about the theoretical properties of side channel distinguishers revealed the rules by which to maximise the probability of first order success (``optimal distinguishers\u27\u27) under different assumptions about the leakage model and noise distribution. Simultaneously, research into bounding first order success (as a function of the number of observations) has revealed universal bounds, which suggest that (even optimal) distinguishers are not able to reach theoretically possible success rates. Is this gap a proof artefact (aka the bounds are not tight) or does a distinguisher exist that is more trace efficient than the ``optimal\u27\u27 one? We show that in the context of an unknown (and not linear) leakage model there is indeed a distinguisher that outperforms the ``optimal\u27\u27 distinguisher in terms of trace efficiency: it is based on the Kruskal-Wallis test

    OccPoIs: Points of Interest based on Neural Network\u27s Key Recovery in Side-Channel Analysis through Occlusion

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    Deep neural networks (DNNs) represent a powerful technique for assessing cryptographic security concerning side-channel analysis (SCA) due to their ability to aggregate leakages automatically, rendering attacks more efficient without preprocessing. Nevertheless, despite their effectiveness, DNNs employed in SCA are predominantly black-box algorithms, posing considerable interpretability challenges. In this paper, we propose a novel technique called Key Guessing Occlusion (KGO) that acquires a minimal set of sample points required by the DNN for key recovery, which we call OccPoIs. These OccPoIs provide information on which areas of the traces are important to the DNN for retrieving the key, enabling evaluators to know where to refine their cryptographic implementation. After obtaining the OccPoIs, we first explore the leakages found in these OccPoIs to understand what the DNN is learning with first-order Correlation Power Analysis (CPA). We show that KGO obtains relevant sample points that have a high correlation with the given leakage model but also acquires sample points that first-order CPA fails to capture. Furthermore, unlike the first-order CPA in the masking setting, KGO obtains these OccPoIs without the knowledge of the shares or mask. Next, we employ the template attack (TA) using the OccPoIs to investigate if KGO could be used as a feature selection tool. We show that using the OccPoIs with TA can recover the key for all the considered synchronized datasets and is consistent as a feature selection tool even on datasets protected by first-order masking. Furthermore, it also allows a more efficient attack than other feature selections on the first-order masking dataset called ASCADf

    Envisioning the Future of Cyber Security in Post-Quantum Era: A Survey on PQ Standardization, Applications, Challenges and Opportunities

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    The rise of quantum computers exposes vulnerabilities in current public key cryptographic protocols, necessitating the development of secure post-quantum (PQ) schemes. Hence, we conduct a comprehensive study on various PQ approaches, covering the constructional design, structural vulnerabilities, and offer security assessments, implementation evaluations, and a particular focus on side-channel attacks. We analyze global standardization processes, evaluate their metrics in relation to real-world applications, and primarily focus on standardized PQ schemes, selected additional signature competition candidates, and PQ-secure cutting-edge schemes beyond standardization. Finally, we present visions and potential future directions for a seamless transition to the PQ era

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    SCMA: Plaintext Classification Assisted Side Channel Spectral Modulation Attacks. Towards Noise-insensitive SCA Attacks...

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    Side-channel analysis (SCA) attacks manifest a significant challenge to the security of cryptographic devices. In turn, it is generally quite expensive to protect from SCAs (energy, area, performance etc.). In this work we exhibit a significant change in paradigm for SCA attacks: our proposed attack is quite different from conventional SCA attacks and is able to filter out physical measurement noise, algorithmic noise, as well as thwart various countermeasures, and extract information from the entire leakage waveform as a whole and not only points-of-interest. We demonstrate on measured devices break of masking schemes of orders 2 and 3, supported by a model and also shuffling and dual-rail based countermeasures model; all performed efficiently with the same methodology, and with orders of magnitude less measurements and smaller computation time; underpinning the importance of this form of attack. In essence, in our attack we assume nothing different than a standard side-channel attack, i.e., a known plaintext scenario. However, we further group and classify leakages associated with specific subsets of plaintexts bits. The fact that we group specific (sub-)plaintexts associated leakages, and than in the next stage group or concatenate the associated leakages of these large groups in a predefined ordered sequence (modulation), enables far stronger attacks against SCA protected and unprotected designs. The evaluation-domain or the modulation-domain is the frequency domain in which per frequency it is possible to build a two feature constellation diagrams (amplitude and phase) and construct distinguishers over these diagrams. On top of the methodological contribution of this new SCA, the main observation we push forward is that practically such an attack is devastating for many countermeasures we were used to consider as secure to some level, such as masking or shuffling with large permutation size. As an example, leakage from a third order masked design can be detected with merely 100 leakage traces from the first statistical moment of the leakage as compared to 15â‹…10615\cdot10^6 traces with conventional SCA leakage detection test from the third statistical order

    ABBY: Automating leakage modeling for side-channels analysis

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    We introduce ABBY, an open-source side-channel leakage profiling framework that targets the microarchitectural layer. Existing solutions to characterize the microarchitectural layer are device-specific and require extensive manual effort. The main innovation of ABBY is the collection of data, which can automatically characterize the microarchitecture of a target device and has the additional benefit of being scalable. Using ABBY, we create two sets of data which capture the interaction of instructions for the ARM CORTEX-M0/M3 architecture. These sets are the first to capture detailed information on the microarchitectural layer. They can be used to explore various leakage models suitable for creating sidechannel leakage simulators. A preliminary evaluation of a leakage model produced with our dataset of real-world cryptographic implementations shows performance comparable to state-of-the-art leakage simulators
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