13 research outputs found

    Vulnerabilities of ``McEliece in the World of Escher

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    Recently, Gligoroski et al. proposed code-based encryption and signature schemes using list decoding, blockwise triangular private keys, and a nonuniform error pattern based on ``generalized error sets. The general approach was referred to as \emph{McEliece in the World of Escher.} This paper demonstrates attacks which are significantly cheaper than the claimed security level of the parameters given by Gligoroski et al. We implemented an attack on the proposed 80-bit parameters which was able to recover private keys for both encryption and signatures in approximately 2 hours on a single laptop. We further find that increasing the parameters to avoid our attack will require parameters to grow by almost an order of magnitude for signatures, and (at least) two orders of magnitude for encryption

    An Encryption Scheme based on Random Split of St-Gen Codes

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    Staircase-Generator codes (St-Gen codes) have recently been introduced in the design of code-based public key schemes and for the design of steganographic matrix embedding schemes. In this paper we propose a method for random splitting of St-Gen Codes and use it to design a new coding based public key encryption scheme. The scheme uses the known list decoding method for St-Gen codes, but introduces a novelty in the creation of the public and private key. We modify the classical approach for hiding the structure of the generator matrix by introducing a technique for splitting it into random parts. This approach counters the weaknesses found in the previous constructions of public key schemes using St-Gen codes. Our initial software implementation shows that encryption using Random Split of St-Gen Codes compared to original St-Gen Codes is slower by a linear factor in the number of random splits of the St-Gen code, while the decryption complexity remains the same

    Information-Set Decoding with Hints

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    This paper studies how to incorporate small information leakages (called โ€œhintsโ€) into information-set decoding (ISD) algorithms. In particular, the influence of these hints on solving the (n, k, t)-syndrome-decoding problem (SDP), i.e., generic syndrome decoding of a code of length n, dimension k, and an error of weight t, is analyzed. We motivate all hints by leakages obtainable through realistic side-channel attacks on code-based post-quantum cryptosystems. One class of studied hints consists of partial knowledge of the error or message, which allow to reduce the length, dimension, or error weight using a suitable transformation of the problem. As a second class of hints, we assume that the Hamming weights of subblocks of the error are known, which can be motivated by a template attack. We present adapted ISD algorithms for this type of leakage. For each third-round code-based NIST submission (Classic McEliece, BIKE, HQC), we show how many hints of each type are needed to reduce the work factor below the claimed security level. E.g., for Classic McEliece mceliece348864, the work factor is reduced below 2^128 for 175 known message entries, 9 known error locations, 650 known error-free positions, or known Hamming weights of 29 subblocks of roughly equal size

    Semantic Security and Key-Privacy With Random Split of St-Gen Codes

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    Recently we have defined Staircase-Generator codes (St-Gen codes) and their variant with a random split of the generator matrix of the codes. One unique property of these codes is that they work with arbitrary error sets. In this paper we give a brief overview of St-Gen codes and the list decoding algorithm for their decoding. We also analyze the semantic security against chosen plaintext attack (IND-CPA) and key-privacy i.e. indistinguishability of public keys under chosen plaintext attack (IK-CPA) of the encryption scheme with random split of St-Gen codes. In a similar manner as it was done by Nojima et al., and later by Yamakawa et al., we show that padding the plaintext with a random bit-string provides IND-CPA and IK-CPA in the standard model. The difference with McEliece scheme is that with our scheme the length of the padded random string is significantly shorter

    A Modified pqsigRM: RM Code-Based Signature Scheme

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    We propose a novel signature scheme based on a modified Reed--Muller (RM) code, which reduces the signing complexity and key size compared to existing code-based signature schemes. This cheme is called as the modified pqsigRM, and corresponds to an improvement of pqsigRM, the proposal submitted to NIST. Courtois, Finiasz, and Sendrier (CFS) proposed a code-based signature scheme using the Goppa codes based on a full domain hash approach. However, owing to the properties of Goppa codes, the CFS signature scheme has drawbacks such as signing complexity and large key size. We overcome these disadvantages of the CFS signature scheme using partially permuted RM code and its decoding, which finds a near codeword for any received vector. Using a partially permuted RM code, the signature scheme resists various known attacks on the RM code-based cryptography. Additionally, we further modify the RM codes by row insertion/deletion of the generator matrix and thereafter resolve the problems reported in the post-quantum cryptography forum by NIST, such as the Hamming weight distribution of the public code

    A Digital Signature Scheme Based on Random Split of St-Gen Codes

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    Recently we proposed a method for a random split of Staircase-Generator codes (St-Gen codes) to counter the weaknesses found in the previous constructions of public key schemes using St-Gen codes. The initial proposal for the random split addressed only the encryption scheme, and we left the problem of how to apply the random splitting on the signature scheme open. In this work we solve that open problem and describe a digital signature scheme based on random split of St-Gen codes

    FuLeeca: A Lee-based Signature Scheme

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    In this work we introduce a new code-based signature scheme, called \textsf{FuLeeca}, based on the NP-hard problem of finding codewords of given Lee-weight. The scheme follows the Hash-and-Sign approach applied to quasi-cyclic codes. Similar approaches in the Hamming metric have suffered statistical attacks, which revealed the small support of the secret basis. Using the Lee metric, we are able to thwart such attacks. We use existing hardness results on the underlying problem and study adapted statistical attacks. We propose parameters for \textsf{FuLeeca}~and compare them to an extensive list of proposed post-quantum secure signature schemes including the ones already standardized by NIST. This comparison reveals that \textsf{FuLeeca}~is competitive. For example, for NIST category I, i.e., 160 bit of classical security, we obtain an average signature size of 1100 bytes and public key sizes of 1318 bytes. Comparing the total communication cost, i.e., the sum of the signature and public key size, we see that \textsf{FuLeeca} is only outperformed by Falcon while the other standardized schemes Dilithium and SPHINCS+ show larger communication costs than \textsf{FuLeeca}

    Wave: A New Family of Trapdoor One-Way Preimage Sampleable Functions Based on Codes

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    We present here a new family of trapdoor one-way Preimage Sampleable Functions (PSF) based on codes, the Wave-PSF family. The trapdoor function is one-way under two computational assumptions: the hardness of generic decoding for high weights and the indistinguishability of generalized (U,U+V)(U,U+V)-codes. Our proof follows the GPV strategy [GPV08]. By including rejection sampling, we ensure the proper distribution for the trapdoor inverse output. The domain sampling property of our family is ensured by using and proving a variant of the left-over hash lemma. We instantiate the new Wave-PSF family with ternary generalized (U,U+V)(U,U+V)-codes to design a "hash-and-sign" signature scheme which achieves existential unforgeability under adaptive chosen message attacks (EUF-CMA) in the random oracle model. For 128 bits of classical security, signature sizes are in the order of 15 thousand bits, the public key size in the order of 4 megabytes, and the rejection rate is limited to one rejection every 10 to 12 signatures.Comment: arXiv admin note: text overlap with arXiv:1706.0806

    ์ •๋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

    Wave: A New Code-Based Signature Scheme

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    preprint IACR disponible sur https://eprint.iacr.org/2018/996/20181022:154324We present here Wave the first "hash-and-sign" code-based signature scheme which strictly follows the GPV strategy [GPV08]. It uses the family of ternary generalized (U, U + V) codes. We prove that Wave achieves existential unforgeability under adaptive chosen message attacks (EUF-CMA) in the random oracle model (ROM) with a tight reduction to two assumptions from coding theory: one is a distinguishing problem that is related to the trapdoor we insert in our scheme, the other one is DOOM, a multiple target version of syndrome decoding. The algorithm produces uniformly distributed signatures through a suitable rejection sampling. Our scheme enjoys efficient signature and verification algorithms. For 128 bits of classical security, signature are 8 thousand bits long and the public key size is slightly smaller than one megabyte. Furthermore, with our current choice of parameters, the rejection rate is limited to one rejection every 3 or 4 signatures
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