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Cloud storage providers can reduce storage costs by detecting identical files and storing only one instance of them. While appealing to the storage providers, this deduplication set-up raises various privacy concerns among clients. Various techniques to retrofit content confidentiality in deduplication have been studied in the literature. Nevertheless, data encryption alone is insufficient to protect...
In this paper, we propose a framework for privacy-preserving outsourced functional computation across large-scale multiple encrypted domains, which we refer to as POFD. With POFD, a user can obtain the output of a function computed over encrypted data from multiple domains while protecting the privacy of the function itself, its input and its output. Specifically, we introduce two notions of POFD,...
We present the problem of privacy-preserving distributed statistical computing (PPDSC) in which one party vertically splits a data set among a set of honest-butcurious clouds and wishes to use the clouds' processing power to perform statistical computation on the overall data set. The cornerstone is to compute covariances and, more specifically, scalar products. Existing protocols for computing scalar...
Storage services based on public clouds provide customers with elastic storage and on-demand accessibility. However, moving data to remote cloud storage also raises privacy concerns. Cryptographic cloud storage and search over encrypted data have attracted attentions from both industry and academics. In this paper, we present a new approach to constructing efficient oblivious keyword search (OKS)...
Query processing that preserves both the data privacy of the owner and the query privacy of the client is a new research problem. It shows increasing importance as cloud computing drives more businesses to outsource their data and querying services. However, most existing studies, including those on data outsourcing, address the data privacy and query privacy separately and cannot be applied to this...
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