LevioSA: Lightweight Secure Arithmetic Computation
We study the problem of secure two-party computation of arithmetic circuits. This problem is motivated by privacy-preserving numerical computations, such as ones arising in the context of machine learning training and classification. 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.
In this work, we design, optimize, and implement an actively secure protocol for secure two-party arithmetic computation. A distinctive feature of our protocol is that it can make a fully modular black-box use of any passively secure implementation of oblivious linear function evaluation (OLE). OLE is a commonly used primitive for secure arithmetic computation, analogously to the role of oblivious transfer in secure Boolean computation.
For typical circuits, our protocol requires roughly 4 invocations of passively secure OLE per multiplication gate. This significantly improves over the recent TinyOLE protocol (Döttling et al., ACM CCS 2017), which requires 22 invocations of actively secure OLE in general, or 44 invocations of a specific code-based passively secure OLE.
We showcase the efficiency of our protocol by applying it to standard benchmarks. Our protocol can be applied for a securely outsourcing neural network classification system. This is the first actively secure implementation of its kind, strengthening the passive security provided by recent related works (Mohassel and Zhang, IEEE S\&P 2017; Juvekar et al., USENIX 2018).
This is joint work with Carmit Hazay, Yuval Ishai and Antonio Marcedone
Muthu Venkitasubramaniam is an Associate Professor at the University of Rochester. He received his BTech degree in computer science from the Indian Institute of Technology, Madras in 2004. He attended Cornell University, where he worked with Rafael Pass receiving his Ph.D. in computer science in 2011. Before arriving at the University of Rochester, he spent a year at the Courant Institute of Mathematical Sciences (NYU) as a postdoctoral researcher supported by the Computing Innovation Fellowship. Muthu's interests are in the theory and practice of Cryptography and Network Security. He is a recipient of the Google Faculty Research award and his work on "L-Diversity: Privacy beyond K-Anonymity" received the ICDE 2017 Influential Paper Award.