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. This paper explicitly shifts the privacy focus in coded computing from protecting user data only against colluding workers to protecting it against the service provider as a whole, by placing the privacy barrier directly at the user side before the data reaches the service provider. To this end, we leverage the recently defined notion of perfect subset privacy, which guarantees zero information leakage from all subsets of the data up to a certain size, and can be obtained via k-wise independence properties of linear codes. Using known techniques from Reed-Muller decoding, we provide a scheme which enables polynomial computation with perfect subset privacy in straggler-free systems. Most importantly, by studying information supersets in Reed-Muller codes, which were scantly studied and may be of independent interest, we extend the previous scheme to tolerate straggling worker nodes inside the service provider.
原文链接
分享
引用
