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15 January 2020
Queen's University Belfast, Center for Secure Information Techonlogies; Belfast, UK
Job PostingClosing date for applications:
Contact: Dr. Jinguang Han (j.han@qub.ac.uk)
More information: https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/post-quantum-anonymous-credential.html
CNRS, IRISA, Rennes, France
Job PostingResearch topic While malware detection and mitigation research are now trending, a lot of challenges and unsolved problems still remain. Recently, sophisticated malware designers invented techniques to circumvent software detection techniques. A new direction consists in using unintentionally emitted hardware side-channel information. The big advantage of this information is the non-detection by malware designers. Still, those approaches have to be established in real-world scenarios and efficient analysis techniques developed and implemented. We are currently building up a realistic IoT malware side-channel analysis platform which gives us first interesting new insights.
Joining our team you will
- infect IoT devices with malware samples,
- be responsible for the maintenance of the side-channel workbench,
- derive and develop efficient implementations of analysis algorithms,
- drive top-quality research and publish in A*/A-class security and malware conferences.
Prerequisites We are looking for team players who are motivated and able to drive top-quality research. The area of research lies between several fields and we expect at least competences in one of them:
- embedded devices/side-channel analysis, and/or
- statistics, machine learning, deep learning, and/or
- malware analysis.
Additionally, an ideal candidate should have:
- Research engineer: MS degree in a related field, with 1-3 years of work experience,
- PostDoc: Ph.D. in a related field
- good programming skills,
- good level in written and spoken English,
- motivation to save the world.
Closing date for applications:
Contact: Annelie Heuser (annelie.heuser@irisa.fr) with a CV, cover letter, and references.
Arpita Patra, Ajith Suresh
ePrint ReportAn extensive benchmarking of BLAZE for the aforementioned ML algorithms over a 64-bit ring in both WAN and LAN settings shows massive improvements over ABY3. Concretely, we observe improvements up to $\mathbf{333\times}$ for Linear Regression, $\mathbf{146 \times}$ for Logistic Regression and $\mathbf{301\times}$ for Neural Networks over WAN. Similarly, we show improvements up to $\mathbf{2610\times}$ for Linear Regression, $\mathbf{820\times}$ for Logistic Regression and $\mathbf{303\times}$ for Neural Networks over LAN.
Aggelos Kiayias, Saad Quader, Alexander Russell
ePrint ReportPedro Maat C. Massolino, Patrick Longa, Joost Renes, Lejla Batina
ePrint ReportD. Robissout, G. Zaid, B. Colombier, L. Bossuet, A. Habrard
ePrint ReportMichail Moraitis, Elena Dubrova
ePrint ReportMatthias Fitzi, Peter Gazi, Aggelos Kiayias, Alexander Russell
ePrint ReportYupu Hu, Siyue Dong, Xingting Dong
ePrint ReportAccording to the assumptions above, Aigis-Enc designers claim that the CPA security of Aigis-Enc is approximately equal to that of the symmetrical LWE scheme in the same scale, and the decryption failure probability of Aigis-Enc is far below that of the symmetrical LWE scheme in the same scale.
In this paper, we make a thorough comparison between Aigis-Enc (with the recommended parameters) and the symmetrical LWE encryption scheme in the same scale. Our conclusion is as followed:
(1) The comparison on CPA security. The formers is 160.898, and the latters is 161.836.
(2) The comparison on computation complexity. In key generation phase, the ratio of the former and the latter on sampling amount of distribution \(\left[ {\begin{array}{*{20}{c}} 0&1\\ {\frac{1}{2}}&{\frac{1}{2}} \end{array}} \right]\) is 5:4; In encryption phase, that ratio is 19:14. The other computations remain the same.
(3) The comparison on decryption failure probability. The formers is $2^{-128.699}$, the latter's is $2^{-67.0582}$. The comparison seems to be dramatic. But in fact, we can slightly increase some traffic to keep failure probability unchanged. In other words, by compressing less to keep decryption failure probability unchanged. In specific: we change the parameters \(\left( {{d_1},{d_2},{d_3}} \right)\) from \(\left( {9,9,4} \right)\) to \(\left( {10,10,4} \right)\), which means a large part of the public key remains the same, the small part of the public key changes from 9 bits per entry into 10bits. A large part of the ciphertext changes from 9 bits per entry into 10 bits, the small part of the ciphertext remains the same. As thus, the communication traffic increases less than $\frac{1}{9}$, while the decryption failure probability is lower than $2^{-128.699}$.
We generalize those attacks presented by designers of Aigis-Enc, including primal attacks and dual attacks. More detailedly, our attacks are more extensive, simpler, and clearer. With them, we obtain the optimal attacks and the optimal-optimal attack on Aigis-Enc and the symmetrical LWE scheme in the same scale.
13 January 2020
Rakyong Choi, Dongyeon Hong, Kwangjo Kim
ePrint ReportTianjun Ma, Haixia Xu, Peili Li
ePrint ReportMohamed Seifelnasr, Hisham S. Galal, Amr M. Youssef
ePrint ReportMahdi Sajadieh, Mohsen Mousavi
ePrint ReportKuan Cheng, Xin Li, Yu Zheng
ePrint ReportHowever, despite extensive research, the tradeoff between the rate of the code and the number of queries is somewhat disappointing. For example, the best known constructions still need super-polynomially long codeword length even with a logarithmic number of queries, and need a polynomial number of queries to achieve a constant rate. In this paper, we show that by using a randomized encoding, in several models we can achieve significantly better rate-query tradeoff. In addition, our codes work for both the standard Hamming errors, and the more general and harder edit errors.
Michael Kounavis, Sergej Deutsch, Santosh Ghosh, David Durham
ePrint ReportSeung Geol Choi, Dana Dachman-Soled, Mukul Kulkarni, Arkady Yerukhimovich
ePrint ReportWe begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple datasets, including the publicly available IP address data set from University of Michigans scans of internet IPv4 space, to determine the tradeoffs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes.
Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms.
Denis Firsov, Ahto Buldas, Ahto Truu, Risto Laanoja
ePrint ReportIn this paper, we report on the machine-checked proofs of existential unforgeability under the chosen-message attack (EUF-CMA) of some variations of BLT digital signature scheme. The proofs are developed and verified using the EasyCrypt framework, which provides interactive theorem proving supported by the state-of-the-art SMT solvers.