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Hard learning problems are important building blocks for the design of various cryptographic functionalities such as authentication protocols and post-quantum public key encryption. The standard implementations of such schemes add some controlled errors to simple (e.g., inner product) computations involving a public challenge and a secret key. Hard physical learning problems formalize the potential gains that could be obtained by leveraging inexact computing to directly generate erroneous samples. While they have good potential for improving the performances and physical security of more conventional samplers when implemented in specialized integrated circuits, it remains unknown whether physical defaults that inevitably occur in their instantiation can lead to security losses, nor whether their implementation can be viable on standard platforms such as FPGAs. We contribute to these questions in the context of the Learning Parity with Physical Noise (LPPN) problem by: (1) exhibiting new (output) data dependencies of the error probabilities that LPPN samples may suffer from; (2) formally showing that LPPN instances with such dependencies are as hard as the standard LPN problem; (3) analyzing an FPGA prototype of LPPN processor that satisfies basic security and performance requirements.

State-of-the-art re-keying schemes can be viewed as a tradeoff between efficient but heuristic solutions based on binary field multiplications, that are only secure if implemented with a sufficient amount of noise, and formal but more expensive solutions based on weak pseudorandom functions, that remain secure if the adversary accesses their output in full. Recent results on “crypto dark matter” (TCC 2018) suggest that low-complexity pseudorandom functions can be obtained by mixing linear functions over different small moduli. In this paper, we conjecture that by mixing some matrix multiplications in a prime field with a physical mapping similar to the leakage functions exploited in side-channel analysis, we can build efficient re-keying schemes based on “crypto-physical dark matter”, that remain secure against an adversary who can access noise-free measurements. We provide first analyzes of the security and implementation properties that such schemes provide. Precisely, we first show that they are more secure than the initial (heuristic) proposal by Medwed et al. (AFRICACRYPT 2010). For example, they can resist attacks put forward by Belaid et al. (ASIACRYPT 2014), satisfy some relevant cryptographic properties and can be connected to a “Learning with Physical Rounding” problem that shares some similarities with standard learning problems. We next show that they are significantly more efficient than the weak pseudorandom function proposed by Dziembowski et al. (CRYPTO 2016), by exhibiting hardware implementation results.

Code-based masking is a very general type of masking scheme that covers Boolean masking, inner product masking, direct sum masking, and so on. The merits of the generalization are twofold. Firstly, the higher algebraic complexity of the sharing function decreases the information leakage in “low noise conditions” and may increase the “statistical security order” of an implementation (with linear leakages). Secondly, the underlying error-correction codes can offer improved fault resistance for the encoded variables. Nevertheless, this higher algebraic complexity also implies additional challenges. On the one hand, a generic multiplication algorithm applicable to any linear code is still unknown. On the other hand, masking schemes with higher algebraic complexity usually come with implementation overheads, as for example witnessed by inner-product masking. In this paper, we contribute to these challenges in two directions. Firstly, we propose a generic algorithm that allows us (to the best of our knowledge for the first time) to compute on data shared with linear codes. Secondly, we introduce a new amortization technique that can significantly mitigate the implementation overheads of code-based masking, and illustrate this claim with a case study. Precisely, we show that, although performing every single code-based masked operation is relatively complex, processing multiple secrets in parallel leads to much better performances. This property enables code-based masked implementations of the AES to compete with the state-of-the-art in randomness complexity. Since our masked operations can be instantiated with various linear codes, we hope that these investigations open new avenues for the study of code-based masking schemes, by specializing the codes for improved performances, better side-channel security or improved fault tolerance., IACR Transactions on Cryptographic Hardware and Embedded Systems, Volume 2020, Issue 2

Improved filter permutators are designed to build stream ciphers that can be efficiently evaluated homomorphically. So far the transciphering with such ciphers has been implemented with homomorphic schemes from the second generation. In theory the third generation is more adapted for the particular design of these ciphers. In this article we study how suitable it is in practice. We implement the transciphering of different instances of the stream cipher family FiLIP with homomorphic encryption schemes of the third generation using the TFHE library. We focus on two kinds of filter for FiLIP. First we consider the direct sum of monomials, already evaluated using HElib and we show the improvements on these results. Then we focus on the XOR-threshold filter, we develop strategies to efficiently evaluate any symmetric Boolean function in an homomorphic way, allowing us to give the first timings for such filters. We investigate different approaches for the homomorphic evaluation: using the leveled homomorphic scheme TGSW, an hybrid approach combining TGSW and TLWE schemes, and the gate boostrapping approach. We discuss the costs in time and memory and the impact on delegation of computation of these different approaches, and we perform a comparison with others transciphering schemes.

We revisit the design of filter permutators as a general approach to build stream ciphers that can be efficiently evaluated in a fully homomorphic manner. We first introduce improved filter permutators that allow better security analyses, instances and implementations than the previously proposed \(\textsf {FLIP}\) family of ciphers. We also put forward the similarities between these improved constructions and a popular PRG design by Goldreich. We then propose a methodology to evaluate the performance of such symmetric cipher designs in a FHE setting, which primarily focuses on the noise level of the symmetric ciphertexts (hence on the amount of operations on these ciphertexts that can be homomorphically evaluated). Evaluations through HElib show that instances of improved filter permutators using direct sums of monomials as filter outperform all existing ciphers in the literature based on this criteria. We also discuss the (limited) overheads of these instances in terms of latency and throughput.

In different contexts such as filtered LFSR, Goldreich’s PRG, and FLIP stream ciphers, the security of a cryptographic primitive mostly depends on the algebraic properties of one Boolean function. Since the Seventies, more and more efficient attacks have been exhibited in this context, related to more and more general algebraic properties, such as the degree, the algebraic immunity, and finally, the fast algebraic immunity. Once the properties to estimate the attack complexities are identified, it remains to determine the exact parameters of interesting family of functions with these properties. Then, these functions can be combined in secondary constructions to guarantee the good algebraic properties of a main function. In particular, the family of symmetric functions, and more precisely the subclass of majority functions, has been intensively studied in the area of cryptography, because of their practical advantages and good properties.

The proliferation of small embedded devices having growing but still limited computing and data storage facilities, and the related development of cloud services with extensive storage and computing means, raise nowadays new privacy issues because of the outsourcing of data processing. This has led to a need for symmetric cryptosystems suited for hybrid symmetric-FHE encryption protocols, ensuring the practicability of the FHE solution. Recent ciphers meant for such use have been introduced, such as LowMC, Kreyvium, FLIP, and Rasta. The introduction of stream ciphers devoted to symmetric-FHE frameworks such as FLIP and its recent modification has in its turn posed new problems on the Boolean functions to be used in them as filter functions. We recall the state of the art in this matter and present further studies (without proof).

International audience; Symmetric ciphers purposed for Fully Homomorphic Encryption FHE have recently been proposed for two main reasons. First, minimizing the implementation time and memory overheads that are inherent to current FHE schemes. Second, improving the homomorphic capacity, i.e. the amount of operations that one can perform on homomorphic ciphertexts before bootstrapping, which amounts to limit their level of noise. Existing solutions for this purpose suggest a gap between block ciphers and stream ciphers. The first ones typically allow a constant but small homomorphic capacity, due to the iteration of rounds eventually leading to complex Boolean functions hence large noise. The second ones typically allow a larger homomorphic capacity for the first ciphertext blocks, that decreases with the number of ciphertext blocks due to the increasing Boolean complexity of the stream ciphers' output. In this paper, we aim to combine the best of these two worlds, and propose a new stream cipher construction that allows constant and smaller noise. Its main idea is to apply a Boolean filter function to a public bit permutation of a constant key register, so that the Boolean complexity of the stream cipher outputs is constant. We also propose an instantiation of the filter function designed to exploit recent 3rd-generation FHE schemes, where the error growth is quasi-additive when adequately multiplying ciphertexts with the same amount of noise. In order to stimulate further investigation, we then specify a few instances of this stream cipher, for which we provide a preliminary security analysis. We finally highlight the good properties of our stream cipher regarding the other goal of minimizing the time and memory complexity of calculus delegation for 2nd-generation FHE﾿schemes. We conclude the paper with open problems related to the large design space opened by these new constructions.