Privacy-Preserving Queries over Relational Databases

By Femi G. Olumofin, Ian Goldberg. 2010

88 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ryan Henry
2. Kim Laine
3. Hao Chen
4. Ian Goldberg
5. Zhicong Huang
6. Casey Devet
7. Wouter Lueks
8. Laurent Fousse

Citations in the corpus

• XPIR : Private Information Retrieval for Everyone (2016, methodology): “Second, the NTT and Ring-LWE based private information retrieval scheme and implementation we describe in this paper offer multi-gigabit processing through-put on a commodity CPU, and an optimizer to automatically setup the system for a given setup (hardware/network/application/security).”
• Revisiting the Computational Practicality of Private Information Retrieval (2011, background): “Besides, multi-server PIR schemes give better performance and are directly deployable in domains where the databases are naturally distributed, such as Internet domain name registration [24].”
• The Best of Both Worlds: Combining Information-Theoretic and Computational PIR for Communication Efficiency (2014, background): “PIR has applications in many privacy-sensitive applications, including patent databases [3], domain name registration [22], anonymous email [27], anonymous communication networks [21], and electronic commerce [17].”
• Optimally Robust Private Information Retrieval (2012, background): “PIR is a fundamental building block for many proposed privacy-sensitive applications in the literature , including patent databases [2], domain name registration [28], anonymous email [33], and improving the scalability of anonymous communication networks [26].”
• Labeled PSI from Fully Homomorphic Encryption with Malicious Security (2018, methodology): “Olumofin and Goldberg [41] used information-theoretical PIR by keywords to protect the privacy of client queries to a public database.”
• Polynomial Batch Codes for Efficient IT-PIR (2016, background): “, by their row number within D); however, prior work shows how one can implement queries that are more expressive, such as retrieval-by-keyword [15] or simple SQL queries [42, 45], atop this basic primitive.”
• One (Block) Size Fits All: PIR and SPIR with Variable-Length Records via Multi-Block Queries (2013, background): “A user’s query encodes a set of keywords [15], the indices of certain records [16], or some simple SQL statements [33], and the database server processes the query and responds without learning any nontrivial information about what data the user is after.”
• Sublinear Scaling for Multi-Client Private Information Retrieval (2015, methodology): “used as a black box to realize more expressive database search functionality, including search by keywords [4] and private SQL queries [16].”