Poster: MaSTer: (Practically) Maliciously Secure Truncation for Replicated Secret Sharing Martin Zbudila Erik Pohle Aysajan Abidin Bart Preneel imec-COSIC, KU Leuven Belgium firstname.lastname@esat.kuleuven.be Abstract—Secure multi-party computation (MPC) in a three-party honest majority scenario is cur- rently the state-of-the-art for running machine learning algorithms in a privacy-preserving man- ner. For efficiency reasons, fixed-point arithmetic is widely used to approximate computation over decimal numbers. In this poster we present a work- in-progress efficient three-party maliciously secure truncation protocol without pre-processing, thereby improving on the current state-of-the-art in MPC over rings using replicated secret sharing (RSS). 1. Introduction Machine learning (ML) and AI dominate the world of data processing, and the need to preserve the privacy of both the model owners’ and users’ data becomes an important objective. Numerous works on privacy-preserving machine learning use advanced cryptographic techniques such as secure multi-party computation (MPC) [2]–[4] to enable confidential training and inference of various machine learning models. In MPC, the state-of-the-art is computation in the three-party setting with honest majority. A key component in evaluating ML algo- rithms over MPC is the efficient computation on secret-shared decimal numbers. Since true float- ing point arithmetic is expensive, an approxima- tion, namely fixed-point arithmetic, is employed. Here, the decimal number is encoded with fixed precision as an integer where the integer size is chosen to accommodate the expected range. Multiplication of two fixed-point integers doubles the length of the fractional part and thus requires efficient truncation afterwards to retain the origi- nal precision. For efficient secret sharing in MPC, the integer is represented in a ring. SecureML [3] first proposed a technique allowing truncation by local operations on the shares in a two-party setting in semi-honest security. The current state- of-the-art three-party maliciously secure trunca- tion was proposed by ABY3 [2], and adopted by follow up works such as Falcon [4]. In the online phase, this truncation requires only one round of communication and only one ring element sent per party. However, the pre-processing phase in- volves a costly evaluation of subtraction circuits, resulting in lower combined throughput. In this poster, we report on a work-in-progress protocol that computes the truncation of fixed- point numbers without pre-processing securely in the presence of one malicious adversary in the three-party setting. Our protocol achieves an overall low cost of two rounds of communication and one ring element sent per party. Idea. Our protocol is based on the two-party trun- cation from SecureML [3]. We first run a semi- honest truncation in the first round and repeat the semi-honest truncation a second time but with a different subset of parties. Finally, the output of the second round is used to verify that no malicious behaviour tampered the result obtained in the first round. In the following, we briefly present necessary preliminaries in Sect. 2 and describe our protocol for truncation in Sect. 3. 2. Preliminaries We begin by presenting the notation used throughout this poster, and discuss related work. 2.1. Notation Given a fixed-point encoded secret x ∈ (−2ℓx, 2ℓx), we define z to be the encoding of