Privacy-Preserving Top-k Keyword Similarity Search over Outsourced Cloud Data

In this paper,we study the problem of privacy-preserving top-k keyword similarity search over outsourced cloud data.Taking edit distance as a measure of similarity,we first build up the similarity keyword sets for all the keywords in the data collection.We then calculate the relevance scores of the elements in the similarity keyword sets by the widely used tf-idf theory.Leveraging both the similarity keyword sets and the relevance scores,we present a new secure and efficient treebased index structure for privacy-preserving top-k keyword similarity search.To prevent potential statistical attacks,we also introduce a two-server model to separate the association between the index structure and the data collection in cloud servers.Thorough analysis is given on the validity of search functionality and formal security proofs are presented for the privacy guarantee of our solution.Experimental results on real-world data sets further demonstrate the availability and efficiency of our solution.

Interfering single photons retreived from collective atomic excitations in two dense cold-atom clouds

We report the Hong–Ou–Mandel（HOM） interference, with visibility of 91%, produced from two independent single photons retrieved from collective atomic excitations in two separate cold-atom clouds with high optical depths of 90. The high visibility of the HOM dip is ascribed to the pure single photon in the Fock state that was generated from a dense-cold-atom cloud pumping by a short pulse. The visibility is always the same regardless of the time response of the single-photon detectors. This result experimentally shows that the single photons retrieved are in a separable temporal state with their idler photons.