16 research outputs found

    A Polynomial-Time Key-Recovery Attack on MQQ Cryptosystems

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    International audienceWe investigate the security of the family of MQQ public key cryptosystems using multivariate quadratic quasigroups (MQQ). These cryptosystems show especially good performance properties. In particular, the MQQ-SIG signature scheme is the fastest scheme in the ECRYPT benchmarking of cryptographic systems (eBACS). We show that both the signature scheme MQQ-SIG and the encryption scheme MQQ-ENC, although using different types of MQQs, share a common algebraic structure that introduces a weakness in both schemes. We use this weakness to mount a successful polynomial time key-recovery attack. Our key-recovery attack finds an equivalent key using the idea of so-called {\it good keys} that reveals the structure gradually. In the process we need to solve a MinRank problem that, because of the structure, can be solved in polynomial-time assuming some mild algebraic assumptions. We highlight that our theoretical results work in characteristic 22 which is known to be the most difficult case to address in theory for MinRank attacks. Also, we emphasize that our attack works without any restriction on the number of polynomials removed from the public-key, that is, using the minus modifier. This was not the case for previous MinRank like-attacks against \MQ\ schemes. From a practical point of view, we are able to break an MQQ-SIG instance of 8080 bits security in less than 22 days, and one of the more conservative MQQ-ENC instances of 128128 bits security in little bit over 99 days. Altogether, our attack shows that it is very hard to design a secure public key scheme based on an easily invertible MQQ structure

    Linearity Measures for MQ Cryptography

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    We propose a new general framework for the security of multivariate quadratic (\mathcal{MQ}) schemes with respect to attacks that exploit the existence of linear subspaces. We adopt linearity measures that have been used traditionally to estimate the security of symmetric cryptographic primitives, namely the nonlinearity measure for vectorial functions introduced by Nyberg at Eurocrypt \u2792, and the (s,t)(s, t)--linearity measure introduced recently by Boura and Canteaut at FSE\u2713. We redefine some properties of \mathcal{MQ} cryptosystems in terms of these known symmetric cryptography notions, and show that our new framework is a compact generalization of several known attacks in \mathcal{MQ} cryptography against single field schemes. We use the framework to explain various pitfalls regarding the successfulness of these attacks. Finally, we argue that linearity can be used as a solid measure for the susceptibility of \mathcal{MQ} schemes to these attacks, and also as a necessary tool for prudent design practice in \mathcal{MQ} cryptography

    Xifrat - Compact Public-Key Cryptosystems based on Quasigroups

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    In this paper, we propose a new public-key cryptosystem based on a quasigroup with the special property of restricted-commutativity . We argue its security empirically and present constructions for key exchange and digital signature. To the best of our knowledge, our primitive and construction have no known polynomial-time attack from quantum computers yet. We note that quasigroups with such property had been independently proposed for use in public-key cryptography and termed entropic quasigroups or entropoids by D.Gligoroski

    Computational Aspects of Retrieving a Representation of an Algebraic Geometry Code

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    Producciรณn CientรญficaCode-based cryptography is an interesting alternative to classic number-theoretic public key cryptosystem since it is conjectured to be secure against quantum computer attacks. Many families of codes have been proposed for these cryptosystems such as algebraic geometry codes. In [Designs, Codes and Cryptography, pages 1-16, 2012] -for so called very strong algebraic geometry codes C=CL(X,P,E)\mathcal C=C_L(\mathcal X, \mathcal P, E), where X\mathcal X is an algebraic curve over Fq\mathbb F_q, P\mathcal P is an nn-tuple of mutually distinct Fq\mathbb F_q-rational points of X\mathcal X and EE is a divisor of X\mathcal X with disjoint support from P\mathcal P --- it was shown that an equivalent representation C=CL(Y,Q,F)\mathcal C=C_L(\mathcal Y, \mathcal Q, F) can be found. The nn-tuple of points is obtained directly from a generator matrix of C\mathcal C, where the columns are viewed as homogeneous coordinates of these points. The curve Y\mathcal Y is given by I2(Y)I_2(\mathcal Y), the homogeneous elements of degree 22 of the vanishing ideal I(Y)I(\mathcal Y). Furthermore, it was shown that I2(Y)I_2(\mathcal Y) can be computed efficiently as the kernel of certain linear map. What was not shown was how to get the divisor FF and how to obtain efficiently an adequate decoding algorithm for the new representation. The main result of this paper is an efficient computational approach to the first problem, that is getting FF. The security status of the McEliece public key cryptosystem using algebraic geometry codes is still not completely settled and is left as an open problemThis research was partly supported by the Danish National Research Foundation and the National Science Foundation of China (Grant No.\ 11061130539) for the Danish-Chinese Center for Applications of Algebraic Geometry in Coding Theory and Cryptography and by Spanish grants MTM2007-64704, MTM2010-21580-C02-02 and MTM2012-36917-C03-03. Part of the research of the second author is also funded by the Vernon Wilson Endowed Chair at Eastern Kentucky University during his sabbatical leave

    A Reaction Attack against Cryptosystems based on LRPC Codes

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    Rank metric is a very promising research direction for code-based cryptography. In fact, thanks to the high complexity of generic decoding attacks against codes in this metric, it is possible to easily select parameters that yield very small data sizes. In this paper we analyze cryptosystems based on Low-Rank Parity-Check (LRPC) codes, one of the classes of codes that are efficiently decodable in the rank metric. We show how to exploit the decoding failure rate, which is an inherent feature of these codes, to devise a reaction attack aimed at recovering the private key. As a case study, we cryptanalyze the recent McNie submission to NISTโ€™s Post-Quantum Standardization process. Additionally, we provide details of a simple implementation to validate our approach

    ์ •๋ณด ๋ณดํ˜ธ ๊ธฐ๊ณ„ ํ•™์Šต์˜ ์•”ํ˜ธํ•™ ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ : ๊ทผ์‚ฌ ๋™ํ˜• ์•”ํ˜ธ์™€ ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์•”ํ˜ธ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋…ธ์ข…์„ .In this dissertation, three main contributions are given as; i) a protocol of privacy-preserving machine learning using network resources, ii) the development of approximate homomorphic encryption that achieves less error and high-precision bootstrapping algorithm without compromising performance and security, iii) the cryptanalysis and the modification of code-based cryptosystems: cryptanalysis on IKKR cryptosystem and modification of the pqsigRM, a digital signature scheme proposed to the post-quantum cryptography (PQC) standardization of National Institute of Standards and Technology (NIST). The recent development of machine learning, cloud computing, and blockchain raises a new privacy problem; how can one outsource computation on confidential data? Moreover, as research on quantum computers shows success, the need for PQC is also emerging. Multi-party computation (MPC) is the cryptographic protocol that makes computation on data without revealing it. Since MPC is designed based on homomorphic encryption (HE) and PQC, research on designing efficient and safe HE and PQC is actively being conducted. First, I propose a protocol for privacy-preserving machine learning (PPML) that replaces bootstrapping of homomorphic encryption with network resources. In general, the HE ciphertext has a limited depth of circuit that can be calculated, called the level of a ciphertext. We call bootstrapping restoring the level of ciphertext that has exhausted its level through a method such as homomorphic decryption. Bootstrapping of homomorphic encryption is, in general, very expensive in time and space. However, when deep computations like deep learning are performed, it is required to do bootstrapping. In this protocol, both the client's message and servers' intermediate values are kept secure, while the client's computation and communication complexity are light. Second, I propose an improved bootstrapping algorithm for the CKKS scheme and a method to reduce the error by homomorphic operations in the CKKS scheme. The Cheon-Kim-Kim-Song (CKKS) scheme (Asiacrypt '17) is one of the highlighted fully homomorphic encryption (FHE) schemes as it is efficient to deal with encrypted real numbers, which are the usual data type for many applications such as machine learning. However, the precision drop due to the error growth is a drawback of the CKKS scheme for data processing. I propose a method to achieve high-precision approximate FHE using the following two methods .First, I apply the signal-to-noise ratio (SNR) concept and propose methods to maximize SNR by reordering homomorphic operations in the CKKS scheme. For that, the error variance is minimized instead of the upper bound of error when we deal with the encrypted data. Second, from the same perspective of minimizing error variance, I propose a new method to find the approximate polynomials for the CKKS scheme. The approximation method is especially applied to the CKKS scheme's bootstrapping, where we achieve bootstrapping with smaller error variance compared to the prior arts. In addition to the above variance-minimizing method, I cast the problem of finding an approximate polynomial for a modulus reduction into an L2-norm minimization problem. As a result, I find an approximate polynomial for the modulus reduction without using the sine function, which is the upper bound for the polynomial approximation of the modulus reduction. By using the proposed method, the constraint of q = O(m^{3/2}) is relaxed as O(m), and thus the level loss in bootstrapping can be reduced. The performance improvement by the proposed methods is verified by implementation over HE libraries, that is, HEAAN and SEAL. The implementation shows that by reordering homomorphic operations and using the proposed polynomial approximation, the reliability of the CKKS scheme is improved. Therefore, the quality of services of various applications using the proposed CKKS scheme, such as PPML, can be improved without compromising performance and security. Finally, I propose an improved code-based signature scheme and cryptanalysis of code-based cryptosystems. A novel code-based signature scheme with small parameters and an attack algorithm on recent code-based cryptosystems are presented in this dissertation. This scheme is based on a modified Reed-Muller (RM) code, which reduces the signing complexity and key size compared with existing code-based signature schemes. The proposed scheme has the advantage of the pqsigRM decoder and uses public codes that are more difficult to distinguish from random codes. I use (U, U+V) -codes with the high-dimensional hull to overcome the disadvantages of code-based schemes. The proposed a decoder which efficiently samples from coset elements with small Hamming weight for any given syndrome. The proposed signature scheme resists various known attacks on RM code-based cryptography. For 128 bits of classical security, the signature size is 4096 bits, and the public key size is less than 1 MB. Recently, Ivanov, Kabatiansky, Krouk, and Rumenko (IKKR) proposed three new variants of the McEliece cryptosystem (CBCrypto 2020, affiliated with Eurocrypt 2020). This dissertation shows that one of the IKKR cryptosystems is equal to the McEliece cryptosystem. Furthermore, a polynomial-time attack algorithm for the other two IKKR cryptosystems is proposed. The proposed attack algorithm utilizes the linearity of IKKR cryptosystems. Also, an implementation of the IKKR cryptosystems and the proposed attack is given. The proposed attack algorithm finds the plaintext within 0.2 sec, which is faster than the elapsed time for legitimate decryption.๋ณธ ๋…ผ๋ฌธ์€ ํฌ๊ฒŒ ๋‹ค์Œ์˜ ์„ธ ๊ฐ€์ง€์˜ ๊ธฐ์—ฌ๋ฅผ ํฌํ•จํ•œ๋‹ค. i) ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•ด์„œ ์ •๋ณด ๋ณดํ˜ธ ๋”ฅ๋Ÿฌ๋‹์„ ๊ฐœ์„ ํ•˜๋Š” ํ”„๋กœํ† ์ฝœ ii) ๊ทผ์‚ฌ ๋™ํ˜• ์•”ํ˜ธ์—์„œ ๋ณด์•ˆ์„ฑ๊ณผ ์„ฑ๋Šฅ์˜ ์†ํ•ด ์—†์ด ์—๋Ÿฌ๋ฅผ ๋‚ฎ์ถ”๊ณ  ๋†’์€ ์ •ํ™•๋„๋กœ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘ ํ•˜๋Š” ๋ฐฉ๋ฒ• iii) IKKR ์•”ํ˜ธ ์‹œ์Šคํ…œ๊ณผ pqsigRM ๋“ฑ ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์•”ํ˜ธ๋ฅผ ๊ณต๊ฒฉํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ ํšจ์œจ์ ์ธ ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์ „์ž ์„œ๋ช… ์‹œ์Šคํ…œ. ๊ทผ๋ž˜์˜ ๊ธฐ๊ณ„ํ•™์Šต๊ณผ ๋ธ”๋ก์ฒด์ธ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ์ธํ•ด์„œ ๊ธฐ๋ฐ€ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์—ฐ์‚ฐ์„ ์–ด๋–ป๊ฒŒ ์™ธ์ฃผํ•  ์ˆ˜ ์žˆ๋Š๋ƒ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๋ณด์•ˆ ๋ฌธ์ œ๊ฐ€ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ์–‘์ž ์ปดํ“จํ„ฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์„ฑ๊ณต์„ ๊ฑฐ๋“ญํ•˜๋ฉด์„œ, ์ด๋ฅผ ์ด์šฉํ•œ ๊ณต๊ฒฉ์— ์ €ํ•ญํ•˜๋Š” ํฌ์ŠคํŠธ ์–‘์ž ์•”ํ˜ธ์˜ ํ•„์š”์„ฑ ๋˜ํ•œ ์ปค์ง€๊ณ  ์žˆ๋‹ค. ๋‹ค์ž๊ฐ„ ์ปดํ“จํŒ…์€ ๋ฐ์ดํ„ฐ๋ฅผ ๊ณต๊ฐœํ•˜์ง€ ์•Š๊ณ  ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ์•”ํ˜ธํ•™์  ํ”„๋กœํ† ์ฝœ์˜ ์ด์นญ์ด๋‹ค. ๋‹ค์ž๊ฐ„ ์ปดํ“จํŒ…์€ ๋™ํ˜• ์•”ํ˜ธ์™€ ํฌ์ŠคํŠธ ์–‘์ž ์•”ํ˜ธ์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์žˆ์œผ๋ฏ€๋กœ, ํšจ์œจ์ ์ธ ๋™ํ˜• ์•”ํ˜ธ์™€ ํฌ์ŠคํŠธ ์–‘์ž ์•”ํ˜ธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํ•˜๊ฒŒ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๋™ํ˜• ์•”ํ˜ธ๋Š” ์•”ํ˜ธํ™”๋œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ์—ฐ์‚ฐ์ด ๊ฐ€๋Šฅํ•œ ํŠน์ˆ˜ํ•œ ์•”ํ˜ธํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๋™ํ˜• ์•”ํ˜ธ์˜ ์•”ํ˜ธ๋ฌธ์— ๋Œ€ํ•ด์„œ ์ˆ˜ํ–‰ ๊ฐ€๋Šฅํ•œ ์—ฐ์‚ฐ์˜ ๊นŠ์ด๊ฐ€ ์ •ํ•ด์ ธ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์•”ํ˜ธ๋ฌธ์˜ ๋ ˆ๋ฒจ์ด๋ผ๊ณ  ์นญํ•œ๋‹ค. ๋ ˆ๋ฒจ์„ ๋ชจ๋‘ ์†Œ๋น„ํ•œ ์•”ํ˜ธ๋ฌธ์˜ ๋ ˆ๋ฒจ์„ ๋‹ค์‹œ ๋ณต์›ํ•˜๋Š” ๊ณผ์ •์„ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘ (bootstrapping)์ด๋ผ๊ณ  ์นญํ•œ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘์€ ๋งค์šฐ ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๋Š” ์—ฐ์‚ฐ์ด๋ฉฐ ์‹œ๊ฐ„ ๋ฐ ๊ณต๊ฐ„ ๋ณต์žก๋„๊ฐ€ ํฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋”ฅ๋Ÿฌ๋‹๊ณผ ๊ฐ™์ด ๊นŠ์ด๊ฐ€ ํฐ ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒฝ์šฐ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ •๋ณด ๋ณดํ˜ธ ๊ธฐ๊ณ„ํ•™์Šต์„ ์œ„ํ•œ ์ƒˆ๋กœ์šด ํ”„๋กœํ† ์ฝœ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ํ”„๋กœํ† ์ฝœ์—์„œ๋Š” ์ž…๋ ฅ ๋ฉ”์‹œ์ง€์™€ ๋”๋ถˆ์–ด ์‹ ๊ฒฝ๋ง์˜ ์ค‘๊ฐ„๊ฐ’๋“ค ๋˜ํ•œ ์•ˆ์ „ํ•˜๊ฒŒ ๋ณดํ˜ธ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์—ฌ์ „ํžˆ ์‚ฌ์šฉ์ž์˜ ํ†ต์‹  ๋ฐ ์—ฐ์‚ฐ ๋ณต์žก๋„๋Š” ๋‚ฎ๊ฒŒ ์œ ์ง€๋œ๋‹ค. Cheon, Kim, Kim ๊ทธ๋ฆฌ๊ณ  Song (CKKS)๊ฐ€ ์ œ์•ˆํ•œ ์•”ํ˜ธ ์‹œ์Šคํ…œ (Asiacrypt 17)์€ ๊ธฐ๊ณ„ํ•™์Šต ๋“ฑ์—์„œ ๊ฐ€์žฅ ๋„๋ฆฌ ์“ฐ์ด๋Š” ๋ฐ์ดํ„ฐ์ธ ์‹ค์ˆ˜๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๋‹ค๋ฃฐ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ๊ฐ€์žฅ ์ด‰๋ง๋ฐ›๋Š” ์™„์ „ ๋™ํ˜• ์•”ํ˜ธ ์‹œ์Šคํ…œ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์˜ค๋ฅ˜์˜ ์ฆํญ๊ณผ ์ „ํŒŒ๊ฐ€ CKKS ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ๊ฐ€์žฅ ํฐ ๋‹จ์ ์ด๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์•„๋ž˜์˜ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ CKKS ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ์˜ค๋ฅ˜๋ฅผ ์ค„์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๋ฉฐ, ์ด๋Š” ๊ทผ์‚ฌ ๋™ํ˜• ์•”ํ˜ธ์— ์ผ๋ฐ˜ํ™”ํ•˜์—ฌ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ฒซ์งธ, ์‹ ํ˜ธ ๋Œ€๋น„ ์žก์Œ ๋น„ (signal-to-noise ratio, SNR)์˜ ๊ฐœ๋…์„ ๋„์ž…ํ•˜์—ฌ, SNR๋ฅผ ์ตœ๋Œ€ํ™”ํ•˜๋„๋ก ์—ฐ์‚ฐ์˜ ์ˆœ์„œ๋ฅผ ์žฌ์กฐ์ •ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๊ธฐ ์œ„ํ•ด์„œ๋Š”, ์˜ค๋ฅ˜์˜ ์ตœ๋Œ€์น˜ ๋Œ€์‹  ๋ถ„์‚ฐ์ด ์ตœ์†Œํ™”๋˜์–ด์•ผ ํ•˜๋ฉฐ, ์ด๋ฅผ ๊ด€๋ฆฌํ•ด์•ผ ํ•œ๋‹ค. ๋‘˜์งธ, ์˜ค๋ฅ˜์˜ ๋ถ„์‚ฐ์„ ์ตœ์†Œํ™”ํ•œ๋‹ค๋Š” ๊ฐ™์€ ๊ด€์ ์—์„œ ์ƒˆ๋กœ์šด ๋‹คํ•ญ์‹ ๊ทผ์‚ฌ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๊ทผ์‚ฌ ๋ฐฉ๋ฒ•์€ ํŠนํžˆ, CKKS ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘์— ์ ์šฉ๋˜์—ˆ์œผ๋ฉฐ, ์ข…๋ž˜ ๊ธฐ์ˆ ๋ณด๋‹ค ๋” ๋‚ฎ์€ ์˜ค๋ฅ˜๋ฅผ ๋‹ฌ์„ฑํ•œ๋‹ค. ์œ„์˜ ๋ฐฉ๋ฒ•์— ๋”ํ•˜์—ฌ, ๊ทผ์‚ฌ ๋‹คํ•ญ์‹์„ ๊ตฌํ•˜๋Š” ๋ฌธ์ œ๋ฅผ L2-norm ์ตœ์†Œํ™” ๋ฌธ์ œ๋กœ ์น˜ํ™˜ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด์„œ ์‚ฌ์ธ ํ•จ์ˆ˜์˜ ๋„์ž… ์—†์ด ๊ทผ์‚ฌ ๋‹คํ•ญ์‹์„ ๊ตฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋ฉด, q=O(m^{3/2})๋ผ๋Š” ์ œ์•ฝ์„ q=O(m)์œผ๋กœ ์ค„์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘์— ํ•„์š”ํ•œ ๋ ˆ๋ฒจ ์†Œ๋ชจ๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ์„ฑ๋Šฅ ํ–ฅ์ƒ์€ HEAAN๊ณผ SEAL ๋“ฑ์˜ ๋™ํ˜• ์•”ํ˜ธ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ํ™œ์šฉํ•œ ๊ตฌํ˜„์„ ํ†ตํ•ด ์ฆ๋ช…ํ–ˆ์œผ๋ฉฐ, ๊ตฌํ˜„์„ ํ†ตํ•ด์„œ ์—ฐ์‚ฐ ์žฌ์ •๋ ฌ๊ณผ ์ƒˆ๋กœ์šด ๋ถ€ํŠธ์ŠคํŠธ๋ž˜ํ•‘์ด CKKS ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณด์•ˆ์„ฑ๊ณผ ์„ฑ๋Šฅ์˜ ํƒ€ํ˜‘ ์—†์ด ๊ทผ์‚ฌ ๋™ํ˜• ์•”ํ˜ธ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ์„œ๋น„์Šค์˜ ์งˆ์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์–‘์ž ์ปดํ“จํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ „ํ†ต์ ์ธ ๊ณต๊ฐœํ‚ค ์•”ํ˜ธ๋ฅผ ๊ณต๊ฒฉํ•˜๋Š” ํšจ์œจ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๊ณต๊ฐœ๋˜๋ฉด์„œ, ํฌ์ŠคํŠธ ์–‘์ž ์•”ํ˜ธ์— ๋Œ€ํ•œ ํ•„์š”์„ฑ์ด ์ฆ๋Œ€ํ–ˆ๋‹ค. ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์•”ํ˜ธ๋Š” ํฌ์ŠคํŠธ ์–‘์ž ์•”ํ˜ธ๋กœ์จ ๋„๋ฆฌ ์—ฐ๊ตฌ๋˜์—ˆ๋‹ค. ์ž‘์€ ํ‚ค ํฌ๊ธฐ๋ฅผ ๊ฐ–๋Š” ์ƒˆ๋กœ์šด ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์ „์ž ์„œ๋ช… ์‹œ์Šคํ…œ๊ณผ ๋ถ€ํ˜ธ ๊ธฐ๋ฐ˜ ์•”ํ˜ธ๋ฅผ ๊ณต๊ฒฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋…ผ๋ฌธ์— ์ œ์•ˆ๋˜์–ด ์žˆ๋‹ค. pqsigRM์ด๋ผ ๋ช…๋ช…ํ•œ ์ „์ž ์„œ๋ช… ์‹œ์Šคํ…œ์ด ๊ทธ๊ฒƒ์ด๋‹ค. ์ด ์ „์ž ์„œ๋ช… ์‹œ์Šคํ…œ์€ ์ˆ˜์ •๋œ Reed-Muller (RM) ๋ถ€ํ˜ธ๋ฅผ ํ™œ์šฉํ•˜๋ฉฐ, ์„œ๋ช…์˜ ๋ณต์žก๋„์™€ ํ‚ค ํฌ๊ธฐ๋ฅผ ์ข…๋ž˜ ๊ธฐ์ˆ ๋ณด๋‹ค ๋งŽ์ด ์ค„์ธ๋‹ค. pqsigRM์€ hull์˜ ์ฐจ์›์ด ํฐ (U, U+V) ๋ถ€ํ˜ธ์™€ ์ด์˜ ๋ณตํ˜ธํ™”๋ฅผ ์ด์šฉํ•˜์—ฌ, ์„œ๋ช…์—์„œ ํฐ ์ด๋“์ด ์žˆ๋‹ค. ์ด ๋ณตํ˜ธํ™” ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ฃผ์–ด์ง„ ๋ชจ๋“  ์ฝ”์…‹ (coset)์˜ ์›์†Œ์— ๋Œ€ํ•˜์—ฌ ์ž‘์€ ํ—ค๋ฐ ๋ฌด๊ฒŒ๋ฅผ ๊ฐ–๋Š” ์›์†Œ๋ฅผ ๋ฐ˜ํ™˜ํ•œ๋‹ค. ๋˜ํ•œ, ์ˆ˜์ •๋œ RM ๋ถ€ํ˜ธ๋ฅผ ์ด์šฉํ•˜์—ฌ, ์•Œ๋ ค์ง„ ๋ชจ๋“  ๊ณต๊ฒฉ์— ์ €ํ•ญํ•œ๋‹ค. 128๋น„ํŠธ ์•ˆ์ •์„ฑ์— ๋Œ€ํ•ด์„œ ์„œ๋ช…์˜ ํฌ๊ธฐ๋Š” 4096 ๋น„ํŠธ์ด๊ณ , ๊ณต๊ฐœ ํ‚ค์˜ ํฌ๊ธฐ๋Š” 1MB๋ณด๋‹ค ์ž‘๋‹ค. ์ตœ๊ทผ, Ivanov, Kabatiansky, Krouk, ๊ทธ๋ฆฌ๊ณ  Rumenko (IKKR)๊ฐ€ McEliece ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ์„ธ ๊ฐ€์ง€ ๋ณ€ํ˜•์„ ๋ฐœํ‘œํ–ˆ๋‹ค (CBCrypto 2020, Eurocrypt 2020์™€ ํ•จ๊ป˜ ์ง„ํ–‰). ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” IKKR ์•”ํ˜ธ ์‹œ์Šคํ…œ์ค‘ ํ•˜๋‚˜๊ฐ€ McEliece ์•”ํ˜ธ ์‹œ์Šคํ…œ๊ณผ ๋™์น˜์ž„์„ ์ฆ๋ช…ํ•œ๋‹ค. ๋˜ํ•œ ๋‚˜๋จธ์ง€ IKKR ์•”ํ˜ธ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ๋‹คํ•ญ ์‹œ๊ฐ„ ๊ณต๊ฒฉ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๊ณต๊ฒฉ์€ IKKR ์•”ํ˜ธ ์‹œ์Šคํ…œ์˜ ์„ ํ˜•์„ฑ์„ ํ™œ์šฉํ•œ๋‹ค. ๋˜ํ•œ, ์ด ๋…ผ๋ฌธ์€ ์ œ์•ˆํ•œ ๊ณต๊ฒฉ์˜ ๊ตฌํ˜„์„ ํฌํ•จํ•˜๋ฉฐ, ์ œ์•ˆ๋œ ๊ณต๊ฒฉ์€ 0.2์ดˆ ์ด๋‚ด์— ๋ฉ”์‹œ์ง€๋ฅผ ๋ณต์›ํ•˜๊ณ , ์ด๋Š” ์ •์ƒ์ ์ธ ๋ณตํ˜ธํ™”๋ณด๋‹ค ๋น ๋ฅธ ์†๋„์ด๋‹ค.Contents Abstract i Contents iv List of Tables ix List of Figures xi 1 Introduction 1 1.1 Homomorphic Encryption and Privacy-Preserving Machine Learning 4 1.2 High-Precision CKKS Scheme and Its Bootstrapping 5 1.2.1 Near-Optimal Bootstrapping of the CKKS Scheme Using Least Squares Method 6 1.2.2 Variance-Minimizing and Optimal Bootstrapping of the CKKS Scheme 8 1.3 Efficient Code-Based Signature Scheme and Cryptanalysis of the Ivanov-Kabatiansky-Krouk-Rumenko Cryptosystems 10 1.3.1 Modified pqsigRM: An Efficient Code-Based Signature Scheme 11 1.3.2 Ivanov-Kabatiansky-Krouk-Rumenko Cryptosystems and Its Equality 13 1.4 Organization of the Dissertation 14 2 Preliminaries 15 2.1 Basic Notation 15 2.2 Privacy-Preserving Machine Learning and Security Terms 16 2.2.1 Privacy-Preserving Machine Learning and Security Terms 16 2.2.2 Privacy-Preserving Machine Learning 17 2.3 The CKKS Scheme and Its Bootstrapping 18 2.3.1 The CKKS Scheme 18 2.3.2 CKKS Scheme in RNS 22 2.3.3 Bootstrapping of the CKKS Scheme 24 2.3.4 Statistical Characteristics of Modulus Reduction and Failure Probability of Bootstrapping of the CKKS Scheme 26 2.4 Approximate Polynomial and Signal-to-Noise Perspective for Approximate Homomorphic Encryption 27 2.4.1 Chebyshev Polynomials 27 2.4.2 Signal-to-Noise Perspective of the CKKS Scheme 28 2.5 Preliminary for Code-Based Cryptography 29 2.5.1 The McEliece Cryptosystem 29 2.5.2 CFS Signature Scheme 30 2.5.3 ReedMuller Codes and Recursive Decoding 31 2.5.4 IKKR Cryptosystems 33 3 Privacy-Preserving Machine Learning via FHEWithout Bootstrapping 37 3.1 Introduction 37 3.2 Information Theoretic Secrecy and HE for Privacy-Preserving Machine Learning 38 3.2.1 The Failure Probability of Ordinary CKKS Bootstrapping 39 3.3 Comparison With Existing Methods 43 3.3.1 Comparison With the Hybrid Method 43 3.3.2 Comparison With FHE Method 44 3.4 Comparison for Evaluating Neural Network 45 4 High-Precision Approximate Homomorphic Encryption and Its Bootstrapping by Error Variance Minimization and Convex Optimization 50 4.1 Introduction 50 4.2 Optimization of Error Variance in the Encrypted Data 51 4.2.1 Tagged Information for Ciphertext 52 4.2.2 WorstCase Assumption 53 4.2.3 Error in Homomorphic Operations of the CKKS Scheme 54 4.2.4 Reordering Homomorphic Operations 59 4.3 Near-Optimal Polynomial for Modulus Reduction 66 4.3.1 Approximate Polynomial Using L2-Norm optimization 66 4.3.2 Efficient Homomorphic Evaluation of the Approximate Polynomial 70 4.4 Optimal Approximate Polynomial and Bootstrapping of the CKKS Scheme 73 4.4.1 Polynomial Basis Error and Polynomial Evaluation in the CKKS Scheme 73 4.4.2 Variance-Minimizing Polynomial Approximation 74 4.4.3 Optimal Approximate Polynomial for Bootstrapping and Magnitude of Its Coefficients 75 4.4.4 Reducing Complexity and Error Using Odd Function 79 4.4.5 Generalization of Weight Constants and Numerical Method 80 4.5 Comparison and Implementation 84 4.6 Reduction of Level Loss in Bootstrapping 89 4.7 Implementation of the Proposed Method and Performance Comparison 92 4.7.1 Error Variance Minimization 92 4.7.2 Weight Constant and Minimum Error Variance 93 4.7.3 Comparison of the Proposed MethodWith the Previous Methods 96 5 Efficient Code-Based Signature Scheme and Cryptanalysis of Code-Based Cryptosystems 104 5.1 Introduction 104 5.2 Modified ReedMuller Codes and Proposed Signature Scheme 105 5.2.1 Partial Permutation of Generator Matrix and Modified ReedMuller Codes 105 5.2.2 Decoding of Modified ReedMuller Codes 108 5.2.3 Proposed Signature Scheme 110 5.3 Security Analysis of Modified pqsigRM 111 5.3.1 Decoding One Out of Many 112 5.3.2 Security Against Key Substitution Attacks 114 5.3.3 EUFCMA Security 114 5.4 Indistinguishability of the Public Code and Signature 120 5.4.1 Modifications of Public Code 121 5.4.2 Public Code Indistinguishability 124 5.4.3 Signature Leaks 126 5.5 Parameter Selection 126 5.5.1 Parameter Sets 126 5.5.2 Statistical Analysis for Determining Number of Partial Permutations 128 5.6 Equivalence of the Prototype IKKR and the McEliece Cryptosystems 131 5.7 Cryptanalysis of the IKKR Cryptosystems 133 5.7.1 Linearity of Two Variants of IKKR Cryptosystems 133 5.7.2 The Attack Algorithm 134 5.7.3 Implementation 135 6 Conclusion 139 6.1 Privacy-Preserving Machine Learning Without Bootstrapping 139 6.2 Variance-Minimization in the CKKS Scheme 140 6.3 L2-Norm Minimization for the Bootstrapping of the CKKS Scheme 141 6.4 Modified pqsigRM: RM Code-Based Signature Scheme 142 6.5 Cryptanalysis of the IKKR Cryptosystem 143 Abstract (In Korean) 155 Acknowlegement 158Docto

    Algebraic Cryptanalysis of STARK-Friendly Designs:Application to MARVELlous and MiMC

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    The block cipher Jarvis and the hash function Friday, both members of the MARVELlous family of cryptographic primitives, are among the first proposed solutions to the problem of designing symmetric-key algorithms suitable for transparent, post-quantum secure zero-knowledge proof systems such as ZK-STARKs. In this paper we describe an algebraic cryptanalysis of Jarvis and Friday and show that the proposed number of rounds is not sufficient to provide adequate security. In Jarvis, the round function is obtained by combining a finite field inversion, a full-degree affine permutation polynomial and a key addition. Yet we show that even though the high degree of the affine polynomial may prevent some algebraic attacks (as claimed by the designers), the particular algebraic properties of the round function make both Jarvis and Friday vulnerable to Grรถbner basis attacks. We also consider MiMC, a block cipher similar in structure to Jarvis. However, this cipher proves to be resistant against our proposed attack strategy. Still, our successful cryptanalysis of Jarvis and Friday does illustrate that block cipher designs for โ€œalgebraic platformsโ€ such as STARKs, FHE or MPC may be particularly vulnerable to algebraic attacks

    Algebraic Cryptanalysis of Deterministic Symmetric Encryption

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    Deterministic symmetric encryption is widely used in many cryptographic applications. The security of deterministic block and stream ciphers is evaluated using cryptanalysis. Cryptanalysis is divided into two main categories: statistical cryptanalysis and algebraic cryptanalysis. Statistical cryptanalysis is a powerful tool for evaluating the security but it often requires a large number of plaintext/ciphertext pairs which is not always available in real life scenario. Algebraic cryptanalysis requires a smaller number of plaintext/ciphertext pairs but the attacks are often underestimated compared to statistical methods. In algebraic cryptanalysis, we consider a polynomial system representing the cipher and a solution of this system reveals the secret key used in the encryption. The contribution of this thesis is twofold. Firstly, we evaluate the performance of existing algebraic techniques with respect to number of plaintext/ciphertext pairs and their selection. We introduce a new strategy for selection of samples. We build this strategy based on cube attacks, which is a well-known technique in algebraic cryptanalysis. We use cube attacks as a fast heuristic to determine sets of plaintexts for which standard algebraic methods, such as Groebner basis techniques or SAT solvers, are more efficient. Secondly, we develop a~new technique for algebraic cryptanalysis which allows us to speed-up existing Groebner basis techniques. This is achieved by efficient finding special polynomials called mutants. Using these mutants in Groebner basis computations and SAT solvers reduces the computational cost to solve the system. Hence, both our methods are designed as tools for building polynomial system representing a cipher. Both tools can be combined and they lead to a significant speedup, even for very simple algebraic solvers

    CRYSTALS-Dilithium: A lattice-based digital signature scheme

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    In this paper, we present the lattice-based signature scheme Dilithium, which is a component of the CRYSTALS (Cryptographic Suite for Algebraic Lattices) suite that was submitted to NISTโ€™s call for post-quantum cryptographic standards. The design of the scheme avoids all uses of discrete Gaussian sampling and is easily implementable in constant-time. For the same security levels, our scheme has a public key that is 2.5X smaller than the previously most efficient lattice-based schemes that did not use Gaussians, while having essentially the same signature size. In addition to the new design, we significantly improve the running time of the main component of many lattice-based constructions โ€“ the number theoretic transform. Our AVX2-based implementation results in a speed-up of roughly a factor of 2 over the previously best algorithms that appear in the literature. The techniques for obtaining this speed-up also have applications to other lattice-based schemes
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