3,294 research outputs found

    Investigating the possibility of leakage detection in water distribution networks using cosmic ray neutrons in the thermal region

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    Water distribution systems can experience high levels of leakage, originating from different sources, such as deterioration due to aging of pipes and fittings, material defects, and corrosion. In addition to causing financial losses and supply problems, leakages in treated water distribution also represent a risk for public health. Despite several techniques for leak detection are already available, there is still a lot of interest in new non-invasive approaches, especially for scenarios where acoustic techniques struggle, such as in noisy environmental conditions. In this work we investigated the possibility of using cosmic ray (CR) neutrons for the detection of underground leakages in water distribution networks, by exploiting the difference in the above ground thermal neutron flux between dry and wet soil conditions. The potential of the technique has been assessed by means of an extensive set of Monte Carlo simulations based on GEANT4, involving realistic scenarios based on the Italian aqueduct design guidelines. Simulation studies focused on sandy soils and results suggest that a significative signal, associated with a leakage, could be detected with a data-taking lasting from a few minutes to a half-hour, depending on the environmental soil moisture, the leaking water distribution in soil, and the soil chemical composition. Finally, a brief description of a new portable and low-cost detector for thermal neutrons, currently under commission, is also presented

    Privacy-preserving artificial intelligence in healthcare: Techniques and applications

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    There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions. [Abstract copyright: Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

    Development of a SQUID magnetometry system for cryogenic neutron electric dipole moment experiment

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    A measurement of the neutron electric dipole moment (nEDM) could hold the key to understanding why the visible universe is the way it is: why matter should predominate over antimatter. As a charge-parity violating (CPV) quantity, an nEDM could provide an insight into new mechanisms that address this baryon asymmetry. The motivation for an improved sensitivity to an nEDM is to find it to be non-zero at a level consistent with certain beyond the Standard Model theories that predict new sources of CPV, or to establish a new limit that constrains them. CryoEDM is an experiment that sought to better the current limit of ∣dn∣<2.9×10−26 e |d_n| < 2.9 \times 10^{-26}\,e\,cm by an order of magnitude. It is designed to measure the nEDM via the Ramsey Method of Separated Oscillatory Fields, in which it is critical that the magnetic field remains stable throughout. A way of accurately tracking the magnetic fields, moreover at a temperature ∼0.5 \sim 0.5\,K, is crucial for CryoEDM, and for future cryogenic projects. This thesis presents work focussing on the development of a 12-SQUID magnetometry system for CryoEDM, that enables the magnetic field to be monitored to a precision of 0.1 0.1\,pT. A major component of its infrastructure is the superconducting capillary shields, which screen the input lines of the SQUIDs from the pick up of spurious magnetic fields that will perturb a SQUID's measurement. These are shown to have a transverse shielding factor of >1×107> 1 \times 10^{7}, which is a few orders of magnitude greater than the calculated requirement. Efforts to characterise the shielding of the SQUID chips themselves are also discussed. The use of Cryoperm for shields reveals a tension between improved SQUID noise and worse neutron statistics. Investigations show that without it, SQUIDs have an elevated noise when cooled in a substantial magnetic field; with it, magnetostatic simulations suggest that it is detrimental to the polarisation of neutrons in transport. The findings suggest that with proper consideration, it is possible to reach a compromise between the two behaviours. Computational work to develop a simulation of SQUID data is detailed, which is based on the Laplace equation for the magnetic scalar potential. These data are ultimately used in the development of a linear regression technique to determine the volume-averaged magnetic field in the neutron cells. This proves highly effective in determining the fields within the 0.1 0.1\,pT requirement under certain conditions

    Exploiting Process Algebras and BPM Techniques for Guaranteeing Success of Distributed Activities

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    The communications and collaborations among activities, pro- cesses, or systems, in general, are the base of complex sys- tems defined as distributed systems. Given the increasing complexity of their structure, interactions, and functionali- ties, many research areas are interested in providing mod- elling techniques and verification capabilities to guarantee their correctness and satisfaction of properties. In particular, the formal methods community provides robust verification techniques to prove system properties. However, most ap- proaches rely on manually designed formal models, making the analysis process challenging because it requires an expert in the field. On the other hand, the BPM community pro- vides a widely used graphical notation (i.e., BPMN) to design internal behaviour and interactions of complex distributed systems that can be enhanced with additional features (e.g., privacy technologies). Furthermore, BPM uses process min- ing techniques to automatically discover these models from events observation. However, verifying properties and ex- pected behaviour, especially in collaborations, still needs a solid methodology. This thesis aims at exploiting the features of the formal meth- ods and BPM communities to provide approaches that en- able formal verification over distributed systems. In this con- text, we propose two approaches. The modelling-based ap- proach starts from BPMN models and produces process al- gebra specifications to enable formal verification of system properties, including privacy-related ones. The process mining- based approach starts from logs observations to automati- xv cally generate process algebra specifications to enable veri- fication capabilities

    Cipherfix: Mitigating Ciphertext Side-Channel Attacks in Software

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    Trusted execution environments (TEEs) provide an environment for running workloads in the cloud without having to trust cloud service providers, by offering additional hardware-assisted security guarantees. However, main memory encryption as a key mechanism to protect against system-level attackers trying to read the TEE's content and physical, off-chip attackers, is insufficient. The recent Cipherleaks attacks infer secret data from TEE-protected implementations by analyzing ciphertext patterns exhibited due to deterministic memory encryption. The underlying vulnerability, dubbed the ciphertext side-channel, is neither protected by state-of-the-art countermeasures like constant-time code nor by hardware fixes. Thus, in this paper, we present a software-based, drop-in solution that can harden existing binaries such that they can be safely executed under TEEs vulnerable to ciphertext side-channels, without requiring recompilation. We combine taint tracking with both static and dynamic binary instrumentation to find sensitive memory locations, and mitigate the leakage by masking secret data before it gets written to memory. This way, although the memory encryption remains deterministic, we destroy any secret-dependent patterns in encrypted memory. We show that our proof-of-concept implementation protects various constant-time implementations against ciphertext side-channels with reasonable overhead.Comment: Jan Wichelmann and Anna P\"atschke contributed equally to this wor

    20th SC@RUG 2023 proceedings 2022-2023

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    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

    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

    Protecting Dilithium against Leakage: Revisited Sensitivity Analysis and Improved Implementations

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    CRYSTALS-Dilithium has been selected by the NIST as the new stan- dard for post-quantum digital signatures. In this work, we revisit the side-channel countermeasures of Dilithium in three directions. First, we improve its sensitivity analysis by classifying intermediate computations according to their physical security requirements. Second, we provide improved gadgets dedicated to Dilithium, taking advantage of recent advances in masking conversion algorithms. Third, we combine these contributions and report performance for side-channel protected Dilithium implementations. Our benchmarking results additionally put forward that the ran- domized version of Dilithium can lead to significantly more efficient implementations (than its deterministic version) when side-channel attacks are a concern

    Deciphering Radio Emission from Solar Coronal Mass Ejections using High-fidelity Spectropolarimetric Radio Imaging

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    Coronal mass ejections (CMEs) are large-scale expulsions of plasma and magnetic fields from the Sun into the heliosphere and are the most important driver of space weather. The geo-effectiveness of a CME is primarily determined by its magnetic field strength and topology. Measurement of CME magnetic fields, both in the corona and heliosphere, is essential for improving space weather forecasting. Observations at radio wavelengths can provide several remote measurement tools for estimating both strength and topology of the CME magnetic fields. Among them, gyrosynchrotron (GS) emission produced by mildly-relativistic electrons trapped in CME magnetic fields is one of the promising methods to estimate magnetic field strength of CMEs at lower and middle coronal heights. However, GS emissions from some parts of the CME are much fainter than the quiet Sun emission and require high dynamic range (DR) imaging for their detection. This thesis presents a state-of-the-art calibration and imaging algorithm capable of routinely producing high DR spectropolarimetric snapshot solar radio images using data from a new technology radio telescope, the Murchison Widefield Array. This allows us to detect much fainter GS emissions from CME plasma at much higher coronal heights. For the first time, robust circular polarization measurements have been jointly used with total intensity measurements to constrain the GS model parameters, which has significantly improved the robustness of the estimated GS model parameters. A piece of observational evidence is also found that routinely used homogeneous and isotropic GS models may not always be sufficient to model the observations. In the future, with upcoming sensitive telescopes and physics-based forward models, it should be possible to relax some of these assumptions and make this method more robust for estimating CME plasma parameters at coronal heights.Comment: 297 pages, 100 figures, 9 tables. Submitted at Tata Institute of Fundamental Research, Mumbai, India, Ph.D Thesi
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