Perfect Subset Privacy in Polynomial Computation via Reed-Muller Information Super-sets
Zirui Deng, Vinayak Ramkumar, Netanel Raviv

TL;DR
This paper introduces a privacy-preserving method for polynomial computation in distributed systems, ensuring zero information leakage from data subsets and extending to tolerate straggling nodes using Reed-Muller codes.
Contribution
It proposes a novel scheme leveraging Reed-Muller decoding for perfect subset privacy in coded computing, addressing privacy risks of the entire service provider.
Findings
Achieves perfect subset privacy with Reed-Muller codes.
Extends privacy scheme to tolerate straggling worker nodes.
Provides a new perspective on privacy in distributed polynomial computation.
Abstract
Delegating large-scale computations to service providers is a common practice which raises privacy concerns. This paper studies information-theoretic privacy-preserving delegation of data to a service provider, who may further delegate the computation to auxiliary worker nodes, in order to compute a polynomial over that data at a later point in time. We study techniques which are compatible with robust management of distributed computation systems, an area known as coded computing. Privacy in coded computing, however, has traditionally addressed the problem of colluding workers, and assumed that the server that administrates the computation is trusted. This viewpoint of privacy does not accurately reflect real-world privacy concerns, since normally, the service provider as a whole (i.e., the administrator and the worker nodes) form one cohesive entity which itself poses a privacy risk.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCryptography and Data Security · Privacy-Preserving Technologies in Data · Complexity and Algorithms in Graphs
