A Hybrid of Dual and Meet-in-the-Middle Attack on Sparse and Ternary Secret LWE

The dual attack is one of the most efficient attack algorithms for the Learning with Errors (LWE) problem. Recently, an efficient variant of the dual attack for sparse and small secret LWE was reported by Albrecht [Eurocrypt 2017], which forces some LWE-based cryptosystems, especially fully homomorphic encryptions (FHE), to change parameters. In this work, we propose a new hybrid of dual and meet-in-the-middle (MITM) attack, which outperforms the improved variant on the same LWE parameter regime. To this end, we adapt the MITM attack for NTRU due to Odlyzko to LWE, and give a rigorous analysis for it. The performance of our MITM attack depends on the relative size of error and modulus, and hence for a large modulus LWE samples, our MITM attack works well for quite large error. We then combine our MITM attack with Albrecht\u27s observation that understands the dual attack as dimension-error tradeoff, which finally yields our hybrid attack. We also implement a sage module that estimates the attack complexity of our algorithm upon {\sf LWE-estimator}, and our attack shows significant performance improvement for the LWE parameter for FHE. For example, for the LWE problem with dimension $n=2^{15}$, modulus $q=2^{628}$ and ternary secret key with Hamming weight 64 which is one parameter set used for {\sf HEAAN} bootstrapping [Eurocrypt 2018], our attack takes $2^{112.5}$ operations and $2^{70.6}$ bit memory while the previous best attack requires $2^{127.2}$ operations as reported by {\sf LWE-estimator}

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...Comment: 12 pages, 4 figure

잡음키를 가지는 신원기반 동형암호에 관한 연구

학위논문(박사)--서울대학교 대학원 :자연과학대학 수리과학부,2020. 2. 천정희.클라우드 상의 데이터 분석 위임 시나리오는 동형암호의 가장 효과적인 응용 시나리오 중 하나이다. 그러나, 다양한 데이터 제공자와 분석결과 요구자가 존재하는 실제 현실의 모델에서는 기본적인 암복호화와 동형 연산 외에도 여전히 해결해야 할 과제들이 남아있는 실정이다. 본 학위논문에서는 이러한 모델에서 필요한 여러 요구사항들을 포착하고, 이에 대한 해결방안을 논하였다. 먼저, 기존의 알려진 동형 데이터 분석 솔루션들은 데이터 간의 층위나 수준을 고려하지 못한다는 점에 착안하여, 신원기반 암호와 동형암호를 결합하여 데이터 사이에 접근 권한을 설정하여 해당 데이터 사이의 연산을 허용하는 모델을 생각하였다. 또한 이 모델의 효율적인 동작을 위해서 동형암호 친화적인 신원기반 암호에 대하여 연구하였고, 기존에 알려진 NTRU 기반의 암호를 확장하여 module-NTRU 문제를 정의하고 이를 기반으로 한 신원기반 암호를 제안하였다. 둘째로, 동형암호의 복호화 과정에는 여전히 비밀키가 관여하고 있고, 따라서 비밀키 관리 문제가 남아있다는 점을 포착하였다. 이러한 점에서 생체정보를 활용할 수 있는 복호화 과정을 개발하여 해당 과정을 동형암호 복호화에 적용하였고, 이를 통해 암복호화와 동형 연산의 전 과정을 어느 곳에도 키가 저장되지 않은 상태로 수행할 수 있는 암호시스템을 제안하였다. 마지막으로, 동형암호의 구체적인 안전성 평가 방법을 고려하였다. 이를 위해 동형암호가 기반하고 있는 이른바 Learning With Errors (LWE) 문제의 실제적인 난해성을 면밀히 분석하였고, 그 결과 기존의 공격 알고리즘보다 평균적으로 1000배 이상 빠른 공격 알고리즘들을 개발하였다. 이를 통해 현재 사용하고 있는 동형암호 파라미터가 안전하지 않음을 보였고, 새로운 공격 알고리즘을 통한 파라미터 설정 방법에 대해서 논하였다.Secure data analysis delegation on cloud is one of the most powerful application that homomorphic encryption (HE) can bring. As the technical level of HE arrive at practical regime, this model is also being considered to be a more serious and realistic paradigm. In this regard, this increasing attention requires more versatile and secure model to deal with much complicated real world problems. First, as real world modeling involves a number of data owners and clients, an authorized control to data access is still required even for HE scenario. Second, we note that although homomorphic operation requires no secret key, the decryption requires the secret key. That is, the secret key management concern still remains even for HE. Last, in a rather fundamental view, we thoroughly analyze the concrete hardness of the base problem of HE, so-called Learning With Errors (LWE). In fact, for the sake of efficiency, HE exploits a weaker variant of LWE whose security is believed not fully understood. For the data encryption phase efficiency, we improve the previously suggested NTRU-lattice ID-based encryption by generalizing the NTRU concept into module-NTRU lattice. Moreover, we design a novel method that decrypts the resulting ciphertext with a noisy key. This enables the decryptor to use its own noisy source, in particular biometric, and hence fundamentally solves the key management problem. Finally, by considering further improvement on existing LWE solving algorithms, we propose new algorithms that shows much faster performance. Consequently, we argue that the HE parameter choice should be updated regarding our attacks in order to maintain the currently claimed security level.1 Introduction 1 1.1 Access Control based on Identity 2 1.2 Biometric Key Management 3 1.3 Concrete Security of HE 3 1.4 List of Papers 4 2 Background 6 2.1 Notation 6 2.2 Lattices 7 2.2.1 Lattice Reduction Algorithm 7 2.2.2 BKZ cost model 8 2.2.3 Geometric Series Assumption (GSA) 8 2.2.4 The Nearest Plane Algorithm 9 2.3 Gaussian Measures 9 2.3.1 Kullback-Leibler Divergence 11 2.4 Lattice-based Hard Problems 12 2.4.1 The Learning With Errors Problem 12 2.4.2 NTRU Problem 13 2.5 One-way and Pseudo-random Functions 14 3 ID-based Data Access Control 16 3.1 Module-NTRU Lattices 16 3.1.1 Construction of MNTRU lattice and trapdoor 17 3.1.2 Minimize the Gram-Schmidt norm 22 3.2 IBE-Scheme from Module-NTRU 24 3.2.1 Scheme Construction 24 3.2.2 Security Analysis by Attack Algorithms 29 3.2.3 Parameter Selections 31 3.3 Application to Signature 33 4 Noisy Key Cryptosystem 36 4.1 Reusable Fuzzy Extractors 37 4.2 Local Functions 40 4.2.1 Hardness over Non-uniform Sources 40 4.2.2 Flipping local functions 43 4.2.3 Noise stability of predicate functions: Xor-Maj 44 4.3 From Pseudorandom Local Functions 47 4.3.1 Basic Construction: One-bit Fuzzy Extractor 48 4.3.2 Expansion to multi-bit Fuzzy Extractor 50 4.3.3 Indistinguishable Reusability 52 4.3.4 One-way Reusability 56 4.4 From Local One-way Functions 59 5 Concrete Security of Homomorphic Encryption 63 5.1 Albrecht's Improved Dual Attack 64 5.1.1 Simple Dual Lattice Attack 64 5.1.2 Improved Dual Attack 66 5.2 Meet-in-the-Middle Attack on LWE 69 5.2.1 Noisy Collision Search 70 5.2.2 Noisy Meet-in-the-middle Attack on LWE 74 5.3 The Hybrid-Dual Attack 76 5.3.1 Dimension-error Trade-o of LWE 77 5.3.2 Our Hybrid Attack 79 5.4 The Hybrid-Primal Attack 82 5.4.1 The Primal Attack on LWE 83 5.4.2 The Hybrid Attack for SVP 86 5.4.3 The Hybrid-Primal attack for LWE 93 5.4.4 Complexity Analysis 96 5.5 Bit-security estimation 102 5.5.1 Estimations 104 5.5.2 Application to PKE 105 6 Conclusion 108 Abstract (in Korean) 120Docto

General Bootstrapping Approach for RLWE-based Homomorphic Encryption

We propose a new bootstrapping approach that works for all three Brakerski-Gentry-Vaikuntanathan (BGV), Brakerski/Fan-Vercauteren (BFV), and Cheon-Kim-Kim-Song (CKKS) schemes. This approach adopts a blind rotation technique from FHEW-type schemes. For BGV and BFV, our bootstrapping does not have any restrictions on plaintext modulus unlike typical cases of the previous methods. For CKKS, our approach introduces an error comparable to a rescaling error which enables more than 70 bits of precision after bootstrapping while consuming only 1-2 levels. Due to the high precision of the proposed bootstrapping algorithm, it is the first bootstrapping resistant to the security vulnerability of CKKS found by Li and Micciancio (Eurocrypt 2021). In addition, we introduce methods to reduce the size of public keys required for blind rotations generated by a secret key holder

Security Guidelines for Implementing Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a cryptographic primitive that allows performing arbitrary operations on encrypted data. Since the conception of the idea in [RAD78], it was considered a holy grail of cryptography. After the first construction in 2009 [Gen09], it has evolved to become a practical primitive with strong security guarantees. Most modern constructions are based on well-known lattice problems such as Learning with Errors (LWE). Besides its academic appeal, in recent years FHE has also attracted significant attention from industry, thanks to its applicability to a considerable number of real-world use-cases. An upcoming standardization effort by ISO/IEC aims to support the wider adoption of these techniques. However, one of the main challenges that standards bodies, developers, and end users usually encounter is establishing parameters. This is particularly hard in the case of FHE because the parameters are not only related to the security level of the system, but also to the type of operations that the system is able to handle. In this paper, we provide examples of parameter sets for LWE targeting particular security levels that can be used in the context of FHE constructions. We also give examples of complete FHE parameter sets, including the parameters relevant for correctness and performance, alongside those relevant for security. As an additional contribution, we survey the parameter selection support offered in open-source FHE libraries

SALSA PICANTE: a machine learning attack on LWE with binary secrets

Learning with Errors (LWE) is a hard math problem underpinning many proposed post-quantum cryptographic (PQC) systems. The only PQC Key Exchange Mechanism (KEM) standardized by NIST is based on module~LWE, and current publicly available PQ Homomorphic Encryption (HE) libraries are based on ring LWE. The security of LWE-based PQ cryptosystems is critical, but certain implementation choices could weaken them. One such choice is sparse binary secrets, desirable for PQ HE schemes for efficiency reasons. Prior work, SALSA, demonstrated a machine learning-based attack on LWE with sparse binary secrets in small dimensions ($n \le 128$) and low Hamming weights ($h \le 4$). However, this attack assumes access to millions of eavesdropped LWE samples and fails at higher Hamming weights or dimensions. We present PICANTE, an enhanced machine learning attack on LWE with sparse binary secrets, which recovers secrets in much larger dimensions (up to $n=350$) and with larger Hamming weights (roughly $n/10$, and up to $h=60$ for $n=350$). We achieve this dramatic improvement via a novel preprocessing step, which allows us to generate training data from a linear number of eavesdropped LWE samples ($4n$) and changes the distribution of the data to improve transformer training. We also improve the secret recovery methods of SALSA and introduce a novel cross-attention recovery mechanism allowing us to read off the secret directly from the trained models. While PICANTE does not threaten NIST\u27s proposed LWE standards, it demonstrates significant improvement over SALSA and could scale further, highlighting the need for future investigation into machine learning attacks on LWE with sparse binary secrets

Notes on Lattice-Based Cryptography

Asymmetrisk kryptering er avhengig av antakelsen om at noen beregningsproblemer er vanskelige å løse. I 1994 viste Peter Shor at de to mest brukte beregningsproblemene, nemlig det diskrete logaritmeproblemet og primtallsfaktorisering, ikke lenger er vanskelige å løse når man bruker en kvantedatamaskin. Siden den gang har forskere jobbet med å finne nye beregningsproblemer som er motstandsdyktige mot kvanteangrep for å erstatte disse to. Gitterbasert kryptografi er forskningsfeltet som bruker kryptografiske primitiver som involverer vanskelige problemer definert på gitter, for eksempel det korteste vektorproblemet og det nærmeste vektorproblemet. NTRU-kryptosystemet, publisert i 1998, var et av de første som ble introdusert på dette feltet. Problemet Learning With Error (LWE) ble introdusert i 2005 av Regev, og det regnes nå som et av de mest lovende beregningsproblemene som snart tas i bruk i stor skala. Å studere vanskelighetsgraden og å finne nye og raskere algoritmer som løser den, ble et ledende forskningstema innen kryptografi. Denne oppgaven inkluderer følgende bidrag til feltet: - En ikke-triviell reduksjon av Mersenne Low Hamming Combination Search Problem, det underliggende problemet med et NTRU-lignende kryptosystem, til Integer Linear Programming (ILP). Særlig finner vi en familie av svake nøkler. - En konkret sikkerhetsanalyse av Integer-RLWE, en vanskelig beregningsproblemvariant av LWE, introdusert av Gu Chunsheng. Vi formaliserer et meet-in-the-middle og et gitterbasert angrep for denne saken, og vi utnytter en svakhet ved parametervalget gitt av Gu, for å bygge et forbedret gitterbasert angrep. - En forbedring av Blum-Kalai-Wasserman-algoritmen for å løse LWE. Mer spesifikt, introduserer vi et nytt reduksjonstrinn og en ny gjetteprosedyre til algoritmen. Disse tillot oss å utvikle to implementeringer av algoritmen, som er i stand til å løse relativt store LWE-forekomster. Mens den første effektivt bare bruker RAM-minne og er fullt parallelliserbar, utnytter den andre en kombinasjon av RAM og disklagring for å overvinne minnebegrensningene gitt av RAM. - Vi fyller et tomrom i paringsbasert kryptografi. Dette ved å gi konkrete formler for å beregne hash-funksjon til G2, den andre gruppen i paringsdomenet, for Barreto-Lynn-Scott-familien av paringsvennlige elliptiske kurver.Public-key Cryptography relies on the assumption that some computational problems are hard to solve. In 1994, Peter Shor showed that the two most used computational problems, namely the Discrete Logarithm Problem and the Integer Factoring Problem, are not hard to solve anymore when using a quantum computer. Since then, researchers have worked on finding new computational problems that are resistant to quantum attacks to replace these two. Lattice-based Cryptography is the research field that employs cryptographic primitives involving hard problems defined on lattices, such as the Shortest Vector Problem and the Closest Vector Problem. The NTRU cryptosystem, published in 1998, was one of the first to be introduced in this field. The Learning With Error (LWE) problem was introduced in 2005 by Regev, and it is now considered one of the most promising computational problems to be employed on a large scale in the near future. Studying its hardness and finding new and faster algorithms that solve it became a leading research topic in Cryptology. This thesis includes the following contributions to the field: - A non-trivial reduction of the Mersenne Low Hamming Combination Search Problem, the underlying problem of an NTRU-like cryptosystem, to Integer Linear Programming (ILP). In particular, we find a family of weak keys. - A concrete security analysis of the Integer-RLWE, a hard computational problem variant of LWE introduced by Gu Chunsheng. We formalize a meet-in-the-middle attack and a lattice-based attack for this case, and we exploit a weakness of the parameters choice given by Gu to build an improved lattice-based attack. - An improvement of the Blum-Kalai-Wasserman algorithm to solve LWE. In particular, we introduce a new reduction step and a new guessing procedure to the algorithm. These allowed us to develop two implementations of the algorithm that are able to solve relatively large LWE instances. While the first one efficiently uses only RAM memory and is fully parallelizable, the second one exploits a combination of RAM and disk storage to overcome the memory limitations given by the RAM. - We fill a gap in Pairing-based Cryptography by providing concrete formulas to compute hash-maps to G2, the second group in the pairing domain, for the Barreto-Lynn-Scott family of pairing-friendly elliptic curves.Doktorgradsavhandlin