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26 April 2021
Graz University of Technology, Graz, Austria
Job PostingThe postdoc position is part the research group of Stefan Mangard. The position is dedicated to basic research in the context of the TU Graz-SAL Dependable Embedded Systems Lab (DES Lab) that aims for new methods for zero-bug software and dependable AI. In the DES Lab she/he will collaborate with SAL (https://silicon-austria-labs.com) and a team TU Graz researchers in the field of cybersecurity, machine learning, formal methods, and embedded systems.
The position offers:
Required Qualifications:
Please send your applications to applications.csbme@tugraz.at while adding the reference: 7050/21/005.
Deadline for the application: May 27th 2021
Closing date for applications:
Contact: In case of questions, feel free to contact Stefan Mangard via email Stefan.Mangard@iaik.tugraz.at.
More information on the DES Lab: https://research-network.silicon-austria.com/des-lab/
University of Luxembourg
Job Posting
The post-docs will be members of the Security and Trust (SnT) research center from the university of Luxembourg (>200 researchers in all aspects of IT security). We offer a competitive salary (about 60,000 euro/year gros). The duration of the position is 2.5 years.
Profile: a PhD in cryptography, with publications in competitive cryptographic conferences
Closing date for applications: June 30th, 2021. We encourage early applications.
Closing date for applications:
Contact: Jean-Sebastien Coron - jean-sebastien.coron at uni dot lu
More information: http://www.crypto-uni.lu/vacancies.html
Virtual event, Anywhere on Earth, 5 October - 8 October 2021
Event CalendarSubmission deadline: 15 May 2021
Notification: 24 June 2021
Madrid, Spain, 7 December - 11 December 2021
Event CalendarSubmission deadline: 30 April 2021
Notification: 25 July 2021
Virtual event, Anywhere on Earth, 14 December - 17 December 2021
Event CalendarSubmission deadline: 16 July 2021
Lübeck, Germany, 11 November - 12 November 2021
Event CalendarSubmission deadline: 25 June 2021
Notification: 30 August 2021
23 April 2021
Françoise Levy-dit-Vehel, Maxime Roméas
ePrint ReportTo solve this problem in the ciphertext-independent setting, we use the Constructive Cryptography (CC) framework defined by Maurer et al. in 2011. We define and construct a resource that we call Updatable Server-Memory Resource (USMR), and study the confidentiality guarantees it achieves when equipped with a UE protocol, that we also model in this framework. With this methodology, we are able to construct resources tailored for each security notion. In particular, we prove that IND-UE-RCCA is the right security notion for many practical UE schemes.
As a consequence, we notably rectify a claim made by Boyd et al., namely that their IND-UE security notion is better than the IND-ENC+UPD notions, in that it hides the age of ciphertexts. We show that this is only true when ciphertexts can leak at most one time per epoch.
We stress that UE security is thought of in the context of adaptive adversaries, and UE schemes should thus bring post-compromise confidentiality guarantees to the client. To handle such adversaries, we use an extension of CC due to Jost et al. and give a clear, simple and composable description of the post-compromise security guarantees of UE schemes. We also model semi-honest adversaries in CC.
Our adaption of the CC framework to UE is generic enough to model other interactive protocols in the outsourced storage setting.
Gang Wang
ePrint ReportLatif AKÇAY, Berna ÖRS
ePrint ReportYanyi Liu, Rafael Pass
ePrint ReportWe finally consider other alternative notions of Kolmogorov complexity---including space-bounded Kolmogorov complexity and conditional Kolmogorov complexity---and show how average-case hardness of problems related to them characterize log-space computable OWFs, or OWFs in $\NC^0$.
Maura B. Paterson, Douglas R. Stinson
ePrint ReportWe also investigate splitting BIBDs that can be "equitably ordered". These splitting BIBDs yield authentication codes with splitting that also have perfect secrecy. We show that, while group generated BIBDs are inherently equitably ordered, the concept is applicable to more general splitting BIBDs. For various pairs $(k,c)$, we determine necessary and sufficient (or almost sufficient) conditions for the existence of $(v, k \times c,1)$-splitting BIBDs that can be equitably ordered. The pairs for which we can solve this problem are $(k,c) = (3,2), (4,2), (3,3)$ and $(3,4)$, as well as all cases with $k = 2$.
Sijun Tan, Brian Knott, Yuan Tian, David J. Wu
ePrint ReportWith CryptGPU, we support private inference and private training on convolutional neural networks with over 60 million parameters as well as handle large datasets like ImageNet. Compared to the previous state-of-the-art, when considering large models and datasets, our protocols achieve a 2x to 8x improvement in private inference and a 6x to 36x improvement for private training. Our work not only showcases the viability of performing secure multiparty computation (MPC) entirely on the GPU to enable fast privacy-preserving machine learning, but also highlights the importance of designing new MPC primitives that can take full advantage of the GPU's computing capabilities.
Tung Chou, Matthias J. Kannwischer, Bo-Yin Yang
ePrint ReportDavid Heath, Vladimir Kolesnikov
ePrint ReportOur scheme LogStack reduces stacked garbling computation from $O(b^2)$ to $O(b \log b)$ with no increase in communication over [HK20a]. The cause of [HK20a]'s increased computation is the oblivious collection of garbage labels that emerge during the evaluation of inactive branches. Garbage is collected by a multiplexer that is costly to generate. At a high level, we redesign stacking and garbage collection to avoid quadratic scaling.
Our construction is also more space efficient: [HK20a] algorithms require $O(b)$ space, while ours use only $O(\log b)$ space. This space efficiency allows even modest setups to handle large numbers of branches.
[HK20a] assumes a random oracle (RO). We track the source of this need, formalize a simple and natural added assumption on the base garbling scheme, and remove reliance on RO: LogStack is secure in the standard model. Nevertheless, LogStack can be instantiated with typical GC tricks based on non-standard assumptions, such as free XOR and half-gates, and hence can be implemented with high efficiency.
We implemented LogStack (in the RO model, based on half-gates garbling) and report performance. In terms of wall-clock time and for fewer than $16$ branches, our performance is comparable to [HK20a]'s; for larger branching factors, our approach clearly outperforms [HK20a]. For example, given $1024$ branches, our approach is $31\times$ faster.
Yuan Yao, Tuna Tufan, Tarun Kathuria, Baris Ege, Ulkuhan Guler, Patrick Schaumont
ePrint ReportNicolas Gailly, Mary Maller, Anca Nitulescu
ePrint ReportThe key tool for our SnarkPack construction is a new commitment scheme that allows us to instantiate the inner product pairing argument of Bünz et al. by using existing powers of tau ceremony transcripts. We also describe a scheme that merge together a multi-exponentiation argument and an inner pairing product argument for some common randomness vector with minimal overhead. We further apply some optimisations to our protocol and illustrate it's efficiency by implementing it. SnarkPack can aggregate 1024 proofs in 2s and verify them in 33ms, including un-serialization time, yielding a verification mechanism that is exponentially faster than batching.
Denis Firsov, Henri Lakk, Ahto Truu
ePrint ReportIn this paper, we construct a stateless tag system with efficient key generation from one-time signature schemes. We prove that the proposed tag system is forward-resistant and when combined with cryptographic timestamping, it induces a secure (existentially unforgeable) multiple-time signature scheme. Our constructions are developed and verified using the EasyCrypt framework.
Michał Wroński
ePrint ReportJorai Rijsdijk, Lichao Wu, Guilherme Perin, Stjepan Picek
ePrint ReportIn this paper, we propose to use reinforcement learning to tune the convolutional neural network hyperparameters. In our framework, we investigate the Q-Learning paradigm and develop two reward functions that use side-channel metrics. We mount an investigation on three commonly used datasets and two leakage models where the results show that reinforcement learning can find convolutional neural networks exhibiting top performance while having small numbers of trainable parameters. We note that our approach is automated and can be easily adapted to different datasets. Several of our newly developed architectures outperform the current state-of-the-art results. Finally, we make our source code publicly available.
Lichao Wu, Guilherme Perin
ePrint ReportThis paper provides extensive experimental results to demonstrate how pooling layer types and pooling stride and size affect the profiling attack performance with convolutional neural networks. Additionally, we demonstrate that pooling hyperparameters can be larger than usually used in related works and still keep good performance for profiling attacks on specific datasets. Finally, with a larger pooling stride and size, a neural network can be reduced in size, favoring training performance.