Differentially Private Access Patterns for Searchable Symmetric Encryption

Author(s):  
Guoxing Chen ◽  
Ten-Hwang Lai ◽  
Michael K. Reiter ◽  
Yinqian Zhang
2021 ◽  
Vol 16 ◽  
pp. 1795-1809
Author(s):  
Qiyang Song ◽  
Zhuotao Liu ◽  
Jiahao Cao ◽  
Kun Sun ◽  
Qi Li ◽  
...  

2015 ◽  
Vol 2015 (2) ◽  
pp. 263-281 ◽  
Author(s):  
Melissa Chase ◽  
Emily Shen

AbstractIn this paper, we consider a setting where a client wants to outsource storage of a large amount of private data and then perform substring search queries on the data – given a data string s and a search string p, find all occurrences of p as a substring of s. First, we formalize an encryption paradigm that we call queryable encryption, which generalizes searchable symmetric encryption (SSE) and structured encryption. Then, we construct a queryable encryption scheme for substring queries. Our construction uses suffix trees and achieves asymptotic efficiency comparable to that of unencrypted suffix trees. Encryption of a string of length n takes O(λn) time and produces a ciphertext of size O(λn), and querying for a substring of length m that occurs k times takes O(λm+k) time and three rounds of communication. Our security definition guarantees correctness of query results and privacy of data and queries against a malicious adversary. Following the line of work started by Curtmola et al. (ACM CCS 2006), in order to construct more efficient schemes we allow the query protocol to leak some limited information that is captured precisely in the definition. We prove security of our substring-searchable encryption scheme against malicious adversaries, where the query protocol leaks limited information about memory access patterns through the suffix tree of the encrypted string.


2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yu Zhang ◽  
Yin Li ◽  
Yifan Wang

Searchable symmetric encryption that supports dynamic multikeyword ranked search (SSE-DMKRS) has been intensively studied during recent years. Such a scheme allows data users to dynamically update documents and retrieve the most wanted documents efficiently. Previous schemes suffer from high computational costs since the time and space complexities of these schemes are linear with the size of the dictionary generated from the dataset. In this paper, by utilizing a shallow neural network model called “Word2vec” together with a balanced binary tree structure, we propose a highly efficient SSE-DMKRS scheme. The “Word2vec” tool can effectively convert the documents and queries into a group of vectors whose dimensions are much smaller than the size of the dictionary. As a result, we can significantly reduce the related space and time cost. Moreover, with the use of the tree-based index, our scheme can achieve a sublinear search time and support dynamic operations like insertion and deletion. Both theoretical and experimental analyses demonstrate that the efficiency of our scheme surpasses any other schemes of the same kind, so that it has a wide application prospect in the real world.


2020 ◽  
Vol 17 (6) ◽  
pp. 1322-1332 ◽  
Author(s):  
Xueqiao Liu ◽  
Guomin Yang ◽  
Yi Mu ◽  
Robert H. Deng

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