The High-Level Practical Overview of Open-Source Privacy-Preserving Machine Learning Solutions

This paper aims to provide a high-level overview of practical approaches to machine-learning respecting the privacy and confidentiality of customer information, which is called Privacy-Preserving Machine Learning. First, the security approaches in offline-learning privacy methods are assessed. Those focused on modern cryptographic methods, such as Homomorphic Encryption and Secure Multi-Party Computation, as well as on dedicated combined hardware and software platforms like Trusted Execution Environment - Intel® Software Guard Extensions (Intel® SGX). Combining the security approaches with different machine learning architectures leads to our Proof of Concept in which the accuracy and speed of the security solutions will be examined. The next step was exploring and comparing the Open-Source Python-based solutions for PPML. Four solutions were selected from almost 40 separate, state-of-the-art systems: SyMPC, TF-Encrypted, TenSEAL, and Gramine. Three different Neural Network architectures were designed to show different libraries’ capabilities. The POC solves the image classification problem based on the MNIST dataset. As the computational results show, the accuracy of all considered secure approaches is similar. The maximum difference between non-secure and secure flow does not exceed 1.2%. In terms of secure computations, the most effective Privacy-Preserving Machine Learning library is based on Trusted Execution Environment, followed by Secure Multi-Party Computation and Homomorphic Encryption. However, most of those are at least 1000 times slower than the non-secure evaluation. Unfortunately, it is not acceptable for a real-world scenario. Future work could combine different security approaches, explore other new and existing state-of-the-art libraries or implement support for hardware-accelerated secure computation

Preimages for Step-Reduced SHA-2

In this paper, we present a preimage attack for 42 step-reduced SHA-256 with time complexity $2^{251.7}$ and memory requirements of order $2^{12}$. The same attack also applies to 42 step-reduced SHA-512 with time complexity $2^{502.3}$ and memory requirements of order $2^{22}$. Our attack is meet-in-the-middle preimage attack

Post Quantum Design in SPDM for Device Authentication and Key Establishment

The Security Protocol and Data Model (SPDM) defines flows to authenticate hardware identity of a computing device. It also allows for establishing a secure session for confidential and integrity protected data communication between two devices. The present version of SPDM, namely version 1.2, relies on traditional asymmetric cryptographic algorithms that are known to be vulnerable to quantum attacks. This paper describes the means by which support for post-quantum (PQ) cryptography can be added to the SPDM protocol in order to enable SPDM for the upcoming world of quantum computing. We examine SPDM 1.2 protocol and discuss how to negotiate the use of post-quantum cryptography algorithms (PQC), how to support device identity reporting, means to authenticate the device, and how to establish a secure session when using PQC algorithms. We consider so called hybrid modes where both classical and PQC algorithms are used to achieve security properties as these modes are important during the transition period. We also share our experience with implementing PQ-SPDM and provide benchmarks for some of the winning NIST PQC algorithms

The MAGIC Mode for Simultaneously Supporting Encryption, Message Authentication and Error Correction

We present MAGIC, a mode for authenticated encryption that simultaneously supports encryption, message authentication and error correction, all with the same code. In MAGIC, the same code employed for cryptographic integrity is also the parity used for error correction. To correct errors, MAGIC employs the Galois Hash transformation, which due to its bit linearity can perform corrections in a similar way as other codes do (e.g., Reed Solomon). To provide a cryptographically strong MAC, MAGIC encrypts the output of the Galois Hash using a secret key. To analyze the security of this construction we adapt the definition of the MAC adversary so that it is applicable to systems that combine message authentication with error correction. We demonstrate that MAGIC offers security in the order of O(2 to the N/2) with N being the tag size

Differential and invertibility properties of BLAKE (full version)

BLAKE is a hash function selected by NIST as one of the 14 second round candidates for the SHA-3 Competition. In this paper, we follow a bottom-up approach to exhibit properties of BLAKE and of its building blocks: based on differential properties of the internal function G, we show that a round of BLAKE is a permutation on the message space, and present an efficient inversion algorithm. For 1.5 rounds we present an algorithm that finds preimages faster than in previous attacks. Discovered properties lead us to describe large classes of impossible differentials for two rounds of BLAKE’s internal permutation, and particular impossible differentials for five and six rounds, respectively for BLAKE- 32 and BLAKE-64. Then, using a linear and rotation-free model, we describe near-collisions for four rounds of the compression function. Finally, we discuss the problem of establishing upper bounds on the probability of differential characteristics for BLAKE

A New Algorithm for the Unbalanced Meet-in-the-Middle Problem

A collision search for a pair of $n$-bit unbalanced functions (one is $R$ times more expensive than the other) is an instance of the meet-in-the-middle problem, solved with the familiar standard algorithm that follows the tradeoff $TM=N$, where $T$ and $M$ are time and memory complexities and $N=2^n$. By combining two ideas, unbalanced interleaving and Oorschot-Wiener parallel collision search, we construct an alternative algorithm that follows $T^2 M = R^2 N$, where $M\le R$. Among others, the algorithm solves the well-known open problem: how to reduce the memory of unbalanced collision search