Private Searching on Streaming Data

By Rafail M. Ostrovsky, William E. Skeith III. 2005

258 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

XPIR : Private Information Retrieval for Everyone (2016, background): “With these parameters, ciphertext size (and thus query element size) is 500Kbits and the expansion factor of encryption is F (cid:39) 5.”
Attribute-based encryption for circuits (2013, methodology): “However, if the attributes were hidden, then this would lead to a general solution for keyword-based search over encrypted data [Boneh et al. 2004; Ostrovsky and Skeith 2005].”
Public Key Encryption That Allows PIR Queries (2007, methodology): “Extensions of the basic PIR primitive (such as [12] [23], mentioned above, and, more recently, [22] [15] [25]) allow more powerful keyword search un-encrypted data.”
Function Secret Sharing: Improvements and Extensions (2016, unknown): “Still relying only on a standard DPF, this can be directly generalized to private search by keywords (returning the payload associated with a private `-bit keyword), private search on streaming data [27, 15, 7], and more.”
PRISM - Privacy-Preserving Search in MapReduce (2012, background): “Private Information Retrieval: Private Information Retrieval (and similarly oblivious transfer and oblivious RAM) has received a lot of attention [9, 13, 19, 22, 29, 32, 33, 35].”
Private Stream Search at the same communication cost as a regular search: Role of LDPC codes (2012, methodology): “Previous Works: The first private stream search algorithm was introduced in 2005 by Ostrovsky and Skeith [1] and makes clever use of homomorphic encryption to hide the content of the query.”
Searchable symmetric encryption: Improved definitions and efficient constructions (2006, unknown): “Speciﬁcally, we point out that IND2-CKA is not an adequate notion of security for SSE and then highlight (and ﬁx) technical issues with Chang and Mitzenmacher’s simulation-based deﬁnition.”
Distributed Point Functions and Their Applications (2014, methodology): “It can easily support database updates and be used in the streaming model of [27].”
High-Speed Private Information Retrieval Computation on GPU (2008, background): “This limits both the database size and the throughput shared by the users, limiting as well their usage for many databases as for other applications such as low-latency unobservable communications [1] or private keyword search [18].”
A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements (2011, background): “Searchable encryption [65], private search [66] 9 9”
Evaluating Branching Programs on Encrypted Data (2007, methodology): “Previous solutions to the secure keyword search problem [9, 13, 30] fall short of achieving the size hiding goal.”
Conjunctive, Subset, and Range Queries on Encrypted Data (2007, background): “Equality searches on streaming data that hide the requested predicate were discussed in [18] and [4].”
The Capacity of Private Computation (2017, background): “Much recent effort is devoted to various computation tasks, such as search [1], [2], matrix multiplication [3], [4] and shufﬂing [3], [5] etc. Privacy is a concern when sensitive data sets are involved.”
Multi-Dimensional Range Query over Encrypted Data (2007, methodology): “This security notion sufﬁces for applications such as network audit logs, and the stock-trading application as described in Section 8.”
Function Secret Sharing (2015, methodology): “We note that by using standard randomized sketching techniques, one can obtain similar solutions that do not only count the number of matches but also return the payloads associated with a bounded number of matches (see, e.g., [41]).”