This work describes a fast fully homomorphic encryption scheme over the torus (TFHE) that revisits, generalizes and improves the fully homomorphic encryption (FHE) based on GSW and its ring variants. The simplest FHE schemes consist in bootstrapped binary gates. In this gate bootstrapping mode, we show that the scheme FHEW of Ducas and Micciancio (Eurocrypt, 2015) can be expressed only in terms of external product between a GSW and an LWE ciphertext. As a consequence of this result and of other optimizations, we decrease the running time of their bootstrapping from 690 to 13 ms single core, using 16 MB bootstrapping key instead of 1 GB, and preserving the security parameter. In leveled homomorphic mode, we propose two methods to manipulate packed data, in order to decrease the ciphertext expansion and to optimize the evaluation of lookup tables and arbitrary functions in $${\mathrm {RingGSW}}$$-based homomorphic schemes. We also extend the automata logic, introduced in Gama et al. (Eurocrypt, 2016), to the efficient leveled evaluation of weighted automata, and present a new homomorphic counter called $$\mathrm {TBSR}$$, that supports all the elementary operations that occur in a multiplication. These improvements speed up the evaluation of most arithmetic functions in a packed leveled mode, with a noise overhead that remains additive. We finally present a new circuit bootstrapping that converts $$\mathsf {LWE}$$ ciphertexts into low-noise $${\mathrm {RingGSW}}$$ ciphertexts in just 137 ms, which makes the leveled mode of TFHE composable and which is fast enough to speed up arithmetic functions, compared to the gate bootstrapping approach. Finally, we provide an alternative practical analysis of LWE based schemes, which directly relates the security parameter to the error rate of LWE and the entropy of the LWE secret key, and we propose concrete parameter sets and timing comparison for all our constructions.

/pdf/tfhe-fast-fully-homomorphic-encryption-over-the-torus-3kwct59cil.pdf

TFHE: Fast Fully Homomorphic Encryption Over the Torus

Ransomware threat success factors, taxonomy, and countermeasures

Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to generalize learning models to reflect future, previously-unseen behaviors. Consequently, most malware classifiers become unsustainable in the long run, becoming rapidly antiquated as malware continues to evolve. In this work, we propose Transcend, a framework to identify aging classification models in vivo during deployment, much before the machine learning model’s performance starts to degrade. This is a significant departure from conventional approaches that retrain aging models retrospectively when poor performance is observed. Our approach uses a statistical comparison of samples seen during deployment with those used to train the model, thereby building metrics for prediction quality. We show how Transcend can be used to identify concept drift based on two separate case studies on Android andWindows malware, raising a red flag before the model starts making consistently poor decisions due to out-of-date training.

https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-jordaney.pdf

Transcend: Detecting Concept Drift in Malware Classification Models

Automated poisoning attacks and defenses in malware detection systems: An adversarial machine learning approach

With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of such features suggest they would unlikely--on their own--be suitable for detecting obfuscated Android malware. To address this challenge, we propose DroidSieve, an Android malware classifier based on static analysis that is fast, accurate, and resilient to obfuscation. For a given app, DroidSieve first decides whether the app is malicious and, if so, classifies it as belonging to a family of related malware. DroidSieve exploits obfuscation-invariant features and artifacts introduced by obfuscation mechanisms used in malware. At the same time, these purely static features are designed for processing at scale and can be extracted quickly. For malware detection, we achieve up to 99.82% accuracy with zero false positives; for family identification of obfuscated malware, we achieve 99.26% accuracy at a fraction of the computational cost of state-of-the-art techniques.

/pdf/droidsieve-fast-and-accurate-classification-of-obfuscated-3hwvtmvtqg.pdf

DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware

We consider all LWE- and NTRU-based encryption, key encapsulation, and digital signature schemes proposed for standardisation as part of the Post-Quantum Cryptography process run by the US National Institute of Standards and Technology (NIST). In particular, we investigate the impact that different estimates for the asymptotic runtime of (block-wise) lattice reduction have on the predicted security of these schemes. Relying on the “LWE estimator” of Albrecht et al., we estimate the cost of running primal and dual lattice attacks against every LWE-based scheme, using every cost model proposed as part of a submission. Furthermore, we estimate the security of the proposed NTRU-based schemes against the primal attack under all cost models for lattice reduction.

Estimate All the {LWE, NTRU} Schemes!

/pdf/estimate-all-the-lwe-ntru-schemes-3i3wgc3mph.pdf

Estimate all the {LWE, NTRU} schemes!

Large Modulus Ring-LWE >= Module-LWE.

In this work, we consider the ring- and module- variants of the LWE problem and investigate cold boot attacks on cryptographic schemes based on these problems, wherein an attacker is faced with the problem of recovering a scheme’s secret key from a noisy version of that key. The leakage resilience of cryptography based on the learning with errors (LWE) problem has been studied before, but there are only limited results considering the parameters observed in cold boot attack scenarios. There are two main encodings for storing ring- and module-LWE keys, and, as we show, the performance of cold boot attacks can be highly sensitive to the exact encoding used. The first encoding stores polynomial coefficients directly in memory. The second encoding performs a number theoretic transform (NTT) before storing the key, a commonly used method leading to more efficient implementations. We first give estimates for a cold boot attack complexity on the first encoding method based on standard algorithms; this analysis confirms that this encoding method is vulnerable to cold boot attacks only at very low bit-flip rates. We then show that, for the second encoding method, the structure introduced by using an NTT is exploitable in the cold boot setting: we develop a bespoke attack strategy that is much cheaper than our estimates for the first encoding when considering module-LWE keys. For example, at a 1% bit-flip rate (which corresponds roughly to what can be achieved in practice for cold boot attacks when applying cooling), a cold boot attack on Kyber KEM parameters has a cost of 243 operations when the second, NTT-based encoding is used for key storage, compared to 270 operations with the first encoding. On the other hand, in the case of the ring-LWE-based KEM, New Hope, the cold boot attack complexities are similar for both encoding methods.

/pdf/cold-boot-attacks-on-ring-and-module-lwe-keys-under-the-ntt-3s1fji4cmp.pdf

Cold Boot Attacks on Ring and Module LWE Keys Under the NTT

Verifiable Oblivious Pseudorandom Functions (VOPRFs) are protocols that allow a client to learn verifiable pseudorandom function (PRF) evaluations on inputs of their choice. The PRF evaluations are computed by a server using their own secret key. The security of the protocol prevents both the server from learning anything about the client’s input, and likewise the client from learning anything about the server’s key. VOPRFs have many applications including password-based authentication, secret-sharing, anonymous authentication and efficient private set intersection. In this work, we construct the first round-optimal (online) VOPRF protocol that retains security from well-known subexponential lattice hardness assumptions. Our protocol requires constructions of non-interactive zero-knowledge arguments of knowledge (NIZKAoK). Using recent developments in the area of post-quantum zero-knowledge arguments of knowledge, we show that our VOPRF may be securely instantiated in the quantum random oracle model. We construct such arguments as extensions of prior work in the area of lattice-based zero-knowledge proof systems.

/pdf/round-optimal-verifiable-oblivious-pseudorandom-functions-22yxkm315q.pdf

Round-Optimal Verifiable Oblivious Pseudorandom Functions from Ideal Lattices

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