International Association
for Cryptologic Research

The School of Computing and Information Technology (SCIT) is looking to recruit two enthusiastic staff members to support teaching and research within SCIT, particularly in the cybersecurity domain, which includes flexible delivery, online degrees and micro-credentials. SCIT aims to maintain its position as a world class Research School and this position is expected to contribute towards that aim.

Closing date for applications:

Contact: Prof. Willy Susilo

More information: https://ejgl.fa.ap1.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/1637/?utm_medium=jobshare

The Services and Cybersecurity (SCS) group at the University of Twente invites applications for a 4-years PhD position on the topic of 'evidence-based security response'.

We are looking for candidates with a solid background in network and system security.

More information and the link to apply:
https://www.utwente.nl/en/organisation/careers/!/147/

Deadline for applications: 20 September 2021, 23:59 CET

Closing date for applications:

Contact: Prof. Dr. Andreas Peter (a.peter@utwente.nl)

More information: https://www.utwente.nl/en/organisation/careers/!/147/

The Telecooperation Lab [TK] at the Technical University of Darmstadt (Prof. Dr. Mühlhäuser) is seeking candidates for a postdoctoral position, preferably in the area of network security, esp. botnet defense. Experts in user-centric security & privacy or quantification of security will also be considered. The contract is initially limited to two years and can be extended
What we offer:

• Highly innovative research, especially within the framework of our participation in the National Research Center for Applied Cybersecurity ATHENE
• Perfection of your research skills using stringent scientific methods
• Independent research as well as research in a team of excellent doctoral and master candidates
• Excellent support for further academic qualification
• Exceptional team spirit and cordial working atmosphere in an international team
• Exposure to cutting-edge research and to an international community of peers

Your profile:

• Appetite for cutting-edge international research and interest to shape the future cybersecurity
• Completed PhD with excellent research record and deep knowledge in one of the stated focus areas
• Experience in writing and publishing scientific work in flagship conferences and journals
• Excellent command of English and preferably good command of German
• Master's level knowledge in computer networks and preferably in artificial intelligence
• Strong interpersonal skills and proven teamwork competencies
• High level of intrinsic motivation and demonstrated ability to perform targeted independent work

Closing date for applications:

Contact: Rolf Egert - egert(at)tk.tu-darmstadt.de

The Department of Computer Science (DIKU) at the University of Copenhagen has an open post-doc position in privacy preserving machine learning, initially with a focus on secure multiparty computation and deep learning.

The post-doc will be located at DIKU, which is part of the Copenhagen ELLIS unit. The research will be conducted in collaboration with cryptography experts at Aarhus University. The application deadline is September 15, 2021.

Closing date for applications:

Contact: Christian Igel (please apply online via https://jobportal.ku.dk/alle-opslag/?show=154272)

More information: https://jobportal.ku.dk/alle-opslag/?show=154272

CHES 2021 will take place virtually on 13-17 September 2021.

The registration site is now open. Registration for CHES 2021 is free for IACR members; non-IACR members will be asked to pay the IACR membership fee (USD 50 regular, USD 25 for students) during registration.

Eurocrypt 2021 will take place in Zagreb, Croatia on October 17-21, 2021 as an in-person conference that will also have support for remote attendees.

The registration site is now open. For in person attendees, please note that the early bird registration will end on September 17th (anywhere on earth). After that deadline, a late registration fee of $100 will be charged.

A number of affiliated events will take place before the main conference. More information can be found here.

Constant function market makers (CFMMs) are the most popular mechanism for facilitating decentralized trading. While these mechanisms have facilitated hundreds of billions of dollars of trades, they provide users with little to no privacy. Recent work illustrates that privacy cannot be achieved in CFMMs without forcing worse pricing and/or latency on end users. This paper more precisely quantifies the trade-off between pricing and privacy in CFMMs. We analyze a simple privacy-enhancing mechanism called Uniform Random Execution and prove that it provides $(\epsilon, \delta)$-differential privacy. The privacy parameter $\epsilon$ depends on the curvature of the CFMM trading function and the number of trades executed. This mechanism can be implemented in any blockchain system that allows smart contracts to access a verifiable random function. We also investigate the worst case complexity over all private CFMM mechanisms using recent results from private PAC learning. These results suggest that one cannot do much better than Uniform Random Execution in CFMMs with non-zero curvature. Our results provide an optimistic outlook on providing partial privacy in CFMMs.

Machine learning as a service (MLaaS) has risen to become a prominent technology due to the large development time, amount of data, hardware costs, and level of expertise required to develop a machine learning model. However, privacy concerns prevent the adoption of MLaaS for applications with sensitive data. One solution to preserve privacy is to use fully homomorphic encryption (FHE) to perform the ML computations. FHE has great power to protect sensitive inputs, and recent advancements have lowered computational costs by several orders of magnitude, allowing for practical applications to be developed. This work looks to optimize FHE-based private machine learning inference by leveraging ternary neural networks. Such neural networks, whose weights are constrained to {-1,0,1}, have special properties that we exploit in this work to operate efficiently in the homomorphic domain. We introduce a general framework that takes an input model, performs plaintext training, and efficiently evaluates private inference leveraging FHE. We perform inference experiments with the MNIST, CIFAR-10, and ImageNet datasets and achieve private inference speeds of only 1.7 to 2.7 orders of magnitude slower compared to their plaintext baseline.

This work presents techniques for modeling Boolean functions by mixed-integer linear inequalities (MILP) on binary variables in-place (without auxiliary variables), reaching minimum possible number of inequalities for small functions and providing meaningful lower bounds on the number of inequalities when reaching the minimum is infeasible. While the minimum number of inequalities does not directly translate to best performance in MILP applications, it nonetheless provides a useful benchmark. We remark that our framework is heuristic and relies on SAT solvers and MILP optimization and so its feasibility is limited.

Individual verifiability remains one of the main practical challenges in e-voting systems and, despite the central importance of this property, countries that sought to implement it faced repeated security problems.

In this note, we revisit this property in the context of the IVXV version of the Estonian voting system, which has been in used for the Estonian municipal elections of 2017 and for the Estonian and European parliamentary elections of 2019.

We show that a compromised voter device can defeat the individual verifiability mechanism of the current Estonian voting system. Our attack takes advantage of the revoting option that is available in the Estonian voting system, and only requires compromise of the voting client application: it does not require compromising the mobile device verification app, or any server side component.

The paper is devoted to the Hadamard square of concatenated linear codes. Such codes consist of codewords that are obtained by concatenation part of the codewords from other codes. It is proved that if the sum of Hadamard squares’ dimensions of the codes used in the concatenation is slightly less than the dimension of the entire space, then the Hadamard square of the concatenated code is equal to the Cartesian product of the Hadamard square of code-components. It means that the cryptanalysis for many code-based post-quantum cryptographic mechanisms built on concatenated codes is equivalent to the cryptanalysis of these mechanisms built on code-components. So using the concatenation of codes from different classes instead of one class of codes, generally speaking, does not increase the cryptographic strength of the mechanisms.

Many decentralized applications require a common source of randomness that cannot be biased by any single party. Randomness beacons provide such a functionality, allowing any (third) party to periodically obtain random values and verify their validity (i.e. check that they are indeed produced by the beacon and consequently random). Protocols implementing randomness beacons have been constructed via a number of different techniques. In particular, several beacons based on time-based cryptography, Publicly Verifiable Secret Sharing (PVSS), Verifiable Random Functions (VRF) and their threshold variant (TVRF) have been proposed. These protocols provide a range of efficiency/randomness quality trade-offs but guarantee security under different setups, assumptions and adversarial models.

In this work, we propose Mt. Random, a multi-tiered randomness beacon that combines PVSS and (T)VRF techniques in order to provide an optimal efficiency/quality trade-off without sacrificing security guarantees. Each tier is based on a different technique and provides a constant stream of random outputs offering progressing efficiency vs. quality trade-offs: true uniform randomness is refreshed less frequently than pseudorandomness, which in turn is refreshed less frequently than (bounded) biased randomness. This wide span of efficiency/quality allows for applications to consume random outputs from an optimal point in this trade-off spectrum. In order to achieve these results, we construct two new building blocks of independent interest: GULL, a PVSS-based beacon that preprocesses a large batch of random outputs but allows for gradual release of smaller ``sub-batches'', which is a first in the literature of randomness beacons; and a publicly verifiable and unbiasable protocol for Distributed Key Generation protocol (DKG), which is significantly more efficient than most of previous DKGs secure under standard assumptions and closely matches the efficiency of the currently most efficient biasable DKG protocol.

Mt. Random (and all of its building blocks) can be proven secure under the standard DDH assumption (in the random oracle model) using only a bulletin board as setup, which is a requirement for the vast majority of beacons. We showcase the efficiency of our novel building blocks and of the Mt. Random beacon via benchmarks made with a prototype implementation. Our experimental results confirm the benefits of our multi-tiered approach, showing that even though higher tiers provide fresh random outputs more often, lower tiers can be executed fast enough to keep higher tiers freshly seeded.

This paper discusses how to analyze the probing security of masked symmetric primitives against the leakage effects from CHES 2018; glitches, transitions, and coupling effects. This is illustrated on several architectures of ciphers like PRESENT, AES, and ASCON where we transform glitch-extended probing secure maskings into transition and/or coupling secure ones. The analysis uses linear cryptanalytic methods and the diffusion layers of the cipher to efficiently protect against the advanced leakage effects.

Threshold Implementations are known countermeasures defending against side-channel attacks via the use of masking techniques. While sufficient properties are known to defend against first-order side-channel attacks, it is not known how to achieve higher-order security. This work generalizes the Threshold Implementation notion of uniformity and proves it achieves second-order protection. The notion is applied to create a second-order masking of the PRESENT cipher with a low randomness cost.

We show polynomial-time quantum algorithms for the following problems: (*) Short integer solution (SIS) problem under the infinity norm, where the public matrix is very wide, the modulus is a polynomially large prime, and the bound of infinity norm is set to be half of the modulus minus a constant. (*) Extrapolated dihedral coset problem (EDCP) with certain parameters. (*) Learning with errors (LWE) problem given LWE-like quantum states with polynomially large moduli and certain error distributions, including bounded uniform distributions and Laplace distributions.

The SIS, EDCP, and LWE problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for those variants.

Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contributions are introducing a filtering technique and solving LWE given LWE-like quantum states with interesting parameters.

Side-channel attacks represent a realistic and serious threat to the security of embedded devices for almost three decades. The variety of attacks and targets they can be applied to have been introduced, and while the area of side-channel attacks and mitigations is very well-researched, it is yet to be consolidated. Deep learning-based side-channel attacks entered the field in recent years with the promise of more competitive performance and enlarged attackers' capabilities compared to other techniques. At the same time, the new attacks bring new challenges and complexities to the domain, making a systematization of the existing knowledge ever more necessary.

In this SoK, we do exactly that, and by bringing new insights, we systematically structure the current knowledge of deep learning in side-channel analysis. We first dissect deep learning-assisted attacks into different phases and map those phases to the efforts conducted so far in the domain. For each of the phases, we identify the weaknesses and challenges that triggered the known open problems.

We connect the attacks to the existing threat models and evaluate their advantages and drawbacks. We finish by discussing other threat models that should be investigated and propose directions for future works.

Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning for strong attack performance, which can be far from trivial. Besides a plethora of options stemming from the machine learning domain, recent years also brought neural network elements specially designed for side-channel analysis.

An important hyperparameter is the loss function, which calculates the error or loss between the actual and desired output. The resulting loss is used to update the weights associated with the connections between the neurons or filters of the deep learning neural network. Unfortunately, despite being a highly relevant hyperparameter, there are no systematic comparisons among different loss functions. This work provides a detailed study on the performance of different loss functions in the SCA context. We evaluate five loss functions commonly used in machine learning and two loss functions proposed for SCA. Our results show that one of the SCA-specific loss functions (called CER) performs very well and outperforms other loss functions in most evaluated settings. Finally, our results show that categorical cross-entropy represents a good option for most settings, especially if there is a requirement to work well with different neural network architectures.

It is well known that several cryptographic primitives cannot be achieved without a common reference string (CRS). Those include, for instance, non-interactive zero-knowledge for NP, or maliciously secure computation in fewer than four rounds. The security of those primitives heavily relies upon on the assumption that the trusted authority, who generates the CRS, does not misuse the randomness used in the CRS generation. However, we argue that there is no such thing as an unconditionally trusted authority and every authority must be held accountable for any trust to be well-founded. Indeed, a malicious authority can, for instance, recover private inputs of honest parties given transcripts of the protocols executed with respect to the CRS it has generated.

While eliminating trust in the trusted authority may not be entirely feasible, can we at least move towards achieving some notion of accountability? We propose a new notion in which, if the CRS authority releases the private inputs of protocol executions to others, we can then provide a publicly-verifiable proof that certifies that the authority misbehaved. We study the feasibility of this notion in the context of non-interactive zero knowledge and two-round secure two-party computation.

In this short note we consider the scheme to share a bitstring secret among $n$ parties such that any $m$ of them, cooperating, can reconstruct it, but any $m - 1$ of them cannot (a so-called $(m,n)$-threshold scheme). The scheme is based on the sound ranging problem, which is to determine the unknown position of the source and the unknown instant when it emitted the sound from known instants when the sound wave reached known sensors. The features are 1) shadows are computed not so much by the secret dealer, but rather by environment where the sound propagates, so the amount of computations performed by the dealer is $O(1)$ instead of $O(n)$ as $n \rightarrow \infty$, and 2) the dealer does not need to establish separate secure channel with each party. There are severe inherent drawbacks though.

The Bitcoin blockchain was the first publicly verifiable, and distributed ledger, where it is possible for everyone to download and check the full history of all data records from the genesis block. These properties lead to the emergence of new types of applications and the redesign of traditional systems that no longer respond to current business needs (e.g., transparency, protection against censorship, decentralization). One particular application is the use of blockchain technology to enable decentralized and self-sovereign identities including new mechanisms for creating, resolving, and revoking them. The public availability of data records has, in turn, paved the way for new kinds of attacks that combine sophisticated heuristics with auxiliary information to compromise users' privacy and deanonymize their identities. In this paper, we review and categorize Bitcoin privacy attacks, investigate their impact on one of the Bitcoin-based identity methods namely did:btcr, and analyze and discuss its privacy properties.