Efficient Integrity Verification for Outsourced Collaborative Filtering

Abstract

Collaborative filtering (CF) over large datasets requires significant computing power. Due to this data owning organizations often outsource the computation of CF (including some abstraction of the data itself) to a public cloud infrastructure. However, this leads to the question of how to verify the integrity of the outsourced computation. In this paper, we develop verification mechanisms for two popular item based collaborative filtering techniques. We further analyze the cheating behavior of the cloud from the game-theoretic perspective. Coupled with the right incentives, we can ensure that the computation is incentive compatible thus ensuring that a rational adversary will not cheat. Leveraging this, we can develop efficient and effective mechanisms to address the problem of integrity in outsourcing.