Numerous applications require continuous publication of statistics for monitoring purposes, such as real-time traffic analysis, timely disease outbreak discovery, and social trends observation. These statistics may be derived from sensitive user data and, hence, necessitate privacy preservation. A notable paradigm for offering strong privacy guarantees in statistics publishing is ϵ-differential privacy. However, there is limited literature that adapts this concept to settings where the statistics are computed over an infinite stream of "events" (i.e., data items generated by the users), and published periodically. These works aim at hiding a single event over the entire stream. We argue that, in most practical scenarios, sensitive information is revealed from multiple events occurring at contiguous time instances. Towards this end, we put forth the novel notion of w-event privacy over infinite streams, which protects any event sequence occurring in w successive time instants. We first formulate our privacy concept, motivate its importance, and introduce a methodology for achieving it. We next design two instantiations, whose utility is independent of the stream length. Finally, we confirm the practicality of our solutions experimenting with real data.

We put forth the problem of delegating the evaluation of a pseudorandom function (PRF) to an untrusted proxy and introduce a novel cryptographic primitive called delegatable pseudorandom functions, or DPRFs for short: A DPRF enables a proxy to evaluate a pseudorandom function (PRF) on a strict subset of its domain using a trapdoor derived from the DPRF secret key. The trapdoor is constructed with respect to a certain policy predicate that determines the subset of input values which the proxy is allowed to compute. The main challenge in constructing DPRFs is to achieve bandwidth efficiency (which mandates that the trapdoor is smaller than the precomputed sequence of the PRF values conforming to the predicate), while maintaining the pseudorandomness of unknown values against an attacker that adaptively controls the proxy. A DPRF may be optionally equipped with an additional property we call policy privacy, where any two delegation predicates remain indistinguishable in the view of a DPRFquerying proxy: achieving this raises new design challenges as policy privacy and bandwidth efficiency are seemingly conflicting goals.For the important class of policy predicates described as (1-dimensional) ranges, we devise two DPRF constructions and rigorously prove their security. Built upon the well-known tree-based GGM PRF family [17], our constructions are generic and feature only logarithmic delegation size in the number of values conforming to the policy predicate. At only a constant-factor efficiency reduction, we show that our second construction is also policy private. Finally, we describe that their new security and efficiency properties render our DPRF schemes particularly useful in numerous security applications, including RFID, symmetric searchable encryption, and broadcast encryption.

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on "practical" private range search (mainly in Databases venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees, or permit unacceptable privacy leakages. In this paper, we take an interdisciplinary approach, which combines the rigor of Security formulations and proofs with efficient Data Management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE) primarily proposed for keyword search. We reduce range search to multikeyword search using range covering techniques with treelike indexes. We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure, and (ii) minimizing false positives incurred by some schemes under heavy data skew. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

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