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Poster: The Petri Dish Attack - Guessing Secrets Based on Bacterial Growth
No full textPINs and unlock patterns remain by far the most
common knowledge-based authentication methods on mobile
devices. Biometric authentication methods such as fingerprints
also rely on PINs and unlock patterns as fallback methods.
In recent years, several attacks on knowledge-based mobile
authentication have been presented, e.g., shoulder-surfing [1],
smudge attacks [2] and thermal attacks [3]. In this poster, we
present the Petri dish attack, a novel attack to guess secrets
based on bacterial growth. We conducted a series of lab
experiments with 20 Petri dishes to evaluate the feasibility of
this new attack and unfortunately were not able to successfully
conduct the attack on off-the-shelf smartphones. However,
we still believe that our results are valuable to the scientific
community and provide a baseline to explore future crossdomain attack vectors and interdisciplinary approaches on
smartphone security
Obfuscation from Low Noise Multilinear Maps
No full textMultilinear maps enable homomorphic computation on encoded values and a public procedure to check if the computation on the encoded values results in a zero. Encodings in known candidate constructions of multilinear maps have a (growing) noise component, which is crucial for security. For example, noise in GGH13 multilinear maps grows with the number of levels that need to be supported and must remain below the maximal noise supported by the multilinear map for correctness. A smaller maximal noise, which must be supported, is desirable both for reasons of security and efficiency.
In this work, we put forward new candidate constructions of obfuscation for which the maximal supported noise is polynomial (in the security parameter). Our constructions are obtained by instantiating a modification of Lin’s obfuscation construction (EUROCRYPT 2016) with composite order variants of the GGH13 multilinear maps. For these schemes, we show that the maximal supported noise only needs to grow polynomially in the security parameter. We prove the security of these constructions in the weak multilinear map model that captures all known vulnerabilities of GGH13 maps. Finally, we investigate the security of the considered composite order variants of GGH13 multilinear maps from a cryptanalytic standpoint
The Complexity of Monitoring Hyperproperties
No full text—We study the runtime verification of hyperproperties, expressed in the temporal logic HyperLTL, as a means to
inspect a system with respect to security polices. Runtime monitors for hyperproperties analyze trace logs that are organized by
common prefixes in the form of a tree-shaped Kripke structure,
or are organized both by common prefixes and by common
suffixes in the form of an acyclic Kripke structure. Unlike runtime
verification techniques for trace properties, where the monitor
tracks the state of the specification but usually does not need
to store traces, a monitor for hyperproperties repeatedly model
checks the growing Kripke structure. This calls for a rigorous
complexity analysis of the model checking problem over treeshaped and acyclic Kripke structures
Design Understanding: From Logic to Specification
Get PDFWe present an outline of the field of Design Understanding and summarize state-of-the-art research in deriving human-understandable knowledge in form of logic properties from an unknown design
Feature Generating Networks for Zero-Shot Learning
No full textSuffering from the extreme training data imbalance be-tween seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state ofthe art on five challenging datasets – CUB, FLO, SUN, AWA and ImageNet – in both the zero-shot learning and generalized zero-shot learning settings
Anonymisierungsverfahren für genetische Daten
No full textPrivacy-Enhancing Technologies (PETs) wie
Differential Privacy und anderen
Anonymisierungsverfahren kommen im Hinblick auf hochsensible
Gesundheitsdaten besondere
Bedeutung zu. Der vorliegende Beitrag zeigt neue Datenschutzrisiken bei
epigenetischen Daten auf, entwickelt und anaIysiert geeignete
Gegenmassnahmen durch PETs und diskutiert die rechtliche Bewertung von
deren Einsatz in der medizinischen Forschung
Privacy-Preserving Similar Patient Queries for Combined Biomedical Data
No full textThe decreasing costs of molecular profiling have fueled the biomedical research community with a plethora of new types of biomedical data, enabling a breakthrough towards more precise and personalized medicine. Naturally, the increasing availability of data also enables physicians to compare patients’ data and treatments easily and to find similar patients in order to propose the optimal therapy. Such similar patient queries (SPQs) are of utmost importance to medical practice and will be relied upon in future health information exchange systems. While privacy-preserving solutions have been previously studied, those are limited to genomic data, ignoring the different newly available types of biomedical data.
In this paper, we propose new cryptographic techniques for finding similar patients in a privacy-preserving manner with various types of biomedical data, including genomic, epigenomic and transcriptomic data as well as their combination. We design protocols for two of the most common similarity metrics in biomedicine: the Euclidean distance and Pearson correlation coefficient. Moreover, unlike previous approaches, we account for the fact that certain locations contribute differently to a given disease or phenotype by allowing to limit the query to the relevant locations and to assign them different weights. Our protocols are specifically designed to be highly efficient in terms of communication and bandwidth, requiring only one or two rounds of communication and thus enabling scalable parallel queries. We rigorously prove our protocols to be secure based on cryptographic games and instantiate our technique with three of the most important types of biomedical data – namely DNA, microRNA expression, and DNA methylation. Our experimental results show that our protocols can compute a similarity query over a typical number of positions against a database of 1,000 patients in a few seconds. Finally, we propose and formalize strategies to mitigate the threat of malicious users or hospitals