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PIR schemes with small download complexity and low storag...
Simon R. Blackburn, Tuvi Etzion, Maura B. Paterson · 2016-09-22 · via cs.CR updates on arXiv.org

In the classical model for (information theoretically secure) Private Information Retrieval (PIR), a user wishes to retrieve one bit of a database that is stored on a set of $n$ servers, in such a way that no individual server gains information about which bit the user is interested in. The aim is to design schemes that minimise communication between the user and the servers. More recently, there have been moves to consider more realistic models where the total storage of the set of servers, or the per server storage, should be minimised (possibly using techniques from distributed storage), and where the database is divided into $R$-bit records with $R>1$, and the user wishes to retrieve one record rather than one bit. When $R$ is large, downloads from the servers to the user dominate the communication complexity and so the aim is to minimise the total number of downloaded bits. Shah, Rashmi and Ramchandran show that at least $R+1$ bits must be downloaded from servers in the worst case, and provide PIR schemes meeting this bound. Sun and Jafar determine the best asymptotic download cost of a PIR scheme (as $R\rightarrow\infty$), where this cost is defined as the ratio of the message length $R$ and the total number of bits downloaded. This paper provides various bounds on the download complexity of a PIR scheme, generalising those of Shah et al. to the case when the number $n$ of servers is bounded, and providing links with classical techniques due to Chor et al. The paper also provides a range of constructions for PIR schemes that are either simpler or perform better than previously known schemes, including explicit schemes that achieve the best asymptotic download complexity of Sun and Jafar with significantly lower upload complexity, and general techniques for constructing a scheme with good worst case download complexity from a scheme with good download complexity on average.