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Shi, Elaine, Stefanov, Emil, Papamanthou, Charalampos.  2013.  Practical Dynamic Proofs of Retrievability. Proceedings of the 2013 ACM SIGSAC Conference on Computer &\#38; Communications Security. :325–336.
Proofs of Retrievability (PoR), proposed by Juels and Kaliski in 2007, enable a client to store n file blocks with a cloud server so that later the server can prove possession of all the data in a very efficient manner (i.e., with constant computation and bandwidth). Although many efficient PoR schemes for static data have been constructed, only two dynamic PoR schemes exist. The scheme by Stefanov et. al. (ACSAC 2012) uses a large of amount of client storage and has a large audit cost. The scheme by Cash (EUROCRYPT 2013) is mostly of theoretical interest, as it employs Oblivious RAM (ORAM) as a black box, leading to increased practical overhead (e.g., it requires about 300 times more bandwidth than our construction). We propose a dynamic PoR scheme with constant client storage whose bandwidth cost is comparable to a Merkle hash tree, thus being very practical. Our construction outperforms the constructions of Stefanov et. al. and Cash et. al., both in theory and in practice. Specifically, for n outsourced blocks of beta bits each, writing a block requires beta+O(lambdalog n) bandwidth and O(betalog n) server computation (lambda is the security parameter). Audits are also very efficient, requiring beta+O(lambda^2log n) bandwidth. We also show how to make our scheme publicly verifiable, providing the first dynamic PoR scheme with such a property. We finally provide a very efficient implementation of our scheme.
Demertzis, Ioannis, Papamanthou, Charalampos.  2017.  Fast Searchable Encryption With Tunable Locality. Proceedings of the 2017 ACM International Conference on Management of Data. :1053–1067.
Searchable encryption (SE) allows a client to outsource a dataset to an untrusted server while enabling the server to answer keyword queries in a private manner. SE can be used as a building block to support more expressive private queries such as range/point and boolean queries, while providing formal security guarantees. To scale SE to big data using external memory, new schemes with small locality have been proposed, where locality is defined as the number of non-continuous reads that the server makes for each query. Previous space-efficient SE schemes achieve optimal locality by increasing the read efficiency-the number of additional memory locations (false positives) that the server reads per result item. This can hurt practical performance. In this work, we design, formally prove secure, and evaluate the first SE scheme with tunable locality and linear space. Our first scheme has optimal locality and outperforms existing approaches (that have a slightly different leakage profile) by up to 2.5 orders of magnitude in terms of read efficiency, for all practical database sizes. Another version of our construction with the same leakage as previous works can be tuned to have bounded locality, optimal read efficiency and up to 60x more efficient end-to-end search time. We demonstrate that our schemes work fast in in-memory as well, leading to search time savings of up to 1 order of magnitude when compared to the most practical in-memory SE schemes. Finally, our construction can be tuned to achieve trade-offs between space, read efficiency, locality, parallelism and communication overhead.
Ghareh Chamani, Javad, Papadopoulos, Dimitrios, Papamanthou, Charalampos, Jalili, Rasool.  2018.  New Constructions for Forward and Backward Private Symmetric Searchable Encryption. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1038-1055.
We study the problem of dynamic symmetric searchable encryption. In that setting, it is crucial to minimize the information revealed to the server as a result of update operations (insertions and deletions). Two relevant privacy properties have been defined in that context: forward and backward privacy. The first makes it hard for the server to link an update operation with previous queries and has been extensively studied in the literature. The second limits what the server can learn about entries that were deleted from the database, from queries that happen after the deletion. Backward privacy was formally studied only recently (Bost et al., CCS 2017) in a work that introduced a formal definition with three variable types of leakage (Type-I to Type-III ordered from most to least secure), as well as the only existing schemes that satisfy this property. In this work, we introduce three novel constructions that improve previous results in multiple ways. The first scheme achieves Type-II backward privacy and our experimental evaluation shows it has 145-253X faster search computation times than previous constructions with the same leakage. Surprisingly, it is faster even than schemes with Type-III leakage which makes it the most efficient implementation of a forward and backward private scheme so far. The second one has search time that is asymptotically within a polylogarithmic multiplicative factor of the theoretical optimal (i.e., the result size of a search), and it achieves the strongest level of backward privacy (Type-I). All previous Type-I constructions require time that is at least linear in the total number of updates for the requested keywords, even the (arbitrarily many) previously deleted ones. Our final scheme improves upon the second one by reducing the number of roundtrips for a search at the cost of extra leakage (Type-III).