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Carmit Hazay

Publications and source records attributed to Carmit Hazay.

3 recordsLinked to original sources

Near-Optimal Communication Byzantine Reliable Broadcast under a Message Adversary

We address the problem of Reliable Broadcast in asynchronous message-passing systems with $n$ nodes, of which up to $t$ are malicious (faulty), in addition to a message adversary that can drop some of the messages sent by correct (non-faulty) nodes. We present a Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) algorithm that communicates ${\cal O}(|m|+nκ)$ bits per node, where $|m|$ represents the length of the application message and $κ=Ω(\log n)$ is a security parameter. This communication complexity is optimal up to the parameter $κ$. This significantly improves upon the state-of-the-art MBRB solution (Albouy, Frey, Raynal, and Taïani, TCS 2023), which incurs communication of ${\cal O}(n|m|+n^2κ)$ bits per node. Our solution sends at most $4n^2$ messages overall, which is asymptotically optimal. Reduced communication is achieved by employing coding techniques that replace the need for all nodes to (re-)broadcast the entire application message $m$. Instead, nodes forward authenticated fragments of the encoding of $m$ using an erasure-correcting code. Under the cryptographic assumptions of threshold signatures and vector commitments, and assuming $n > 3t+2d$, where the adversary drops at most $d$ messages per broadcast, our algorithm allows at least $\ell = n - t - (1 + ε)d$ (for any arbitrarily low $ε> 0$) correct nodes to reconstruct $m$, despite missing fragments caused by the malicious nodes and the message adversary.

cs.DC

Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation

In several settings of practical interest, two parties seek to collaboratively perform inference on their private data using a public machine learning model. For instance, several hospitals might wish to share patient medical records for enhanced diagnostics and disease prediction, but may not be able to share data in the clear because of privacy concerns. In this work, we propose an actively secure protocol for outsourcing secure and private machine learning computations. Recent works on the problem have mainly focused on passively secure protocols, whose security holds against passive (`semi-honest') parties but may completely break down in the presence of active (`malicious') parties who can deviate from the protocol. Secure neural networks based classification algorithms can be seen as an instantiation of an arithmetic computation over integers. We showcase the efficiency of our protocol by applying it to real-world instances of arithmetized neural network computations, including a network trained to perform collaborative disease prediction.

cs.CR

On Compression of Data Encrypted with Block Ciphers

This paper investigates compression of data encrypted with block ciphers, such as the Advanced Encryption Standard (AES). It is shown that such data can be feasibly compressed without knowledge of the secret key. Block ciphers operating in various chaining modes are considered and it is shown how compression can be achieved without compromising security of the encryption scheme. Further, it is shown that there exists a fundamental limitation to the practical compressibility of block ciphers when no chaining is used between blocks. Some performance results for practical code constructions used to compress binary sources are presented.

cs.IT