Paper 2021/317

MPCCache: Privacy-Preserving Multi-Party Cooperative Cache Sharing at the Edge

Duong Tung Nguyen and Ni Trieu

Abstract

Edge computing and caching have emerged as key technologies in the future communication network to enhance the user experience, reduce backhaul traffic, and enable various Internet of Things applications. Different from conventional resources like CPU and memory that can be utilized by only one party at a time, a cached data item, which can be considered as a public good, can serve multiple parties simultaneously. Therefore, instead of independent caching, it is beneficial for the parties (e.g., Telcos) to cooperate and proactively store their common items in a shared cache that can be accessed by all the parties at the same time. In this work, we present MPCCache, a novel privacy-preserving Multi-party Cooperative Cache sharing framework, which allows multiple network operators to determine a set of common data items with the highest access frequencies to be stored in their capacity-limited shared cache while guaranteeing the privacy of their individual datasets. The technical core of our MPCCache is a new construction that allows multiple parties to compute a specific function on the intersection set of their datasets, without revealing the intersection itself to any party. We evaluate our protocols to demonstrate their practicality and show that MPCCache scales well to large datasets and achieves a few hundred times faster compared to a baseline scheme that optimally combines existing MPC protocols.