Lifting Private Information Retrieval from Two to any Number of Messages

By Rafael G. L. D'Oliveira, Salim el Rouayheb. 2018

17 citations recorded by the source corpus.

We study private information retrieval (PIR) on coded data with possibly colluding servers. Devising PIR schemes with optimal download rate in the case of collusion and coded data is still open in general. We provide a lifting operation that can transform what we call one-shot PIR schemes for two messages into schemes for any number of messages. We apply this lifting operation on existing PIR schemes and describe two immediate implications. First, we obtain novel PIR schemes with improved download rate in the case of MDS coded data and server collusion. Second, we provide a simplified description of existing optimal PIR schemes on replicated data as lifted secret sharing based PIR.

Subfields: Artificial Intelligence, Computer Networks and Communications, Privacy & Security, Information Theory

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In Artificial Intelligence

In Computer Networks and Communications

In Privacy & Security

In Information Theory

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Further work has explored the connection between PIR and secret sharing in the past [410] with recent connections to PIR on coded data [139] and communication efficient PIR [66] having been established.”