Information sharing across private databases

By Rakesh Agrawal, Alexandre V. Evfimievski, Rayadurgam Srikant. ACM SIGMOD Conference, 2003

757 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/ff5d7a068400788fd55ae7401e6dd34091e7d737

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Citations in the corpus

Privacy-Preserving Set Operations (2005, methodology): “The most relevant work to our paper is by Freedman, Nissim, and Pinkas (FNP) [14] and Rakesh Agrawal and Alexandre Evfimievski and Ramakrishnan Srikant [1].”
Privacy-Preserving Data Linkage and Geocoding: Current Approaches and Research Directions (2006, background): “Two-party protocols for minimal information sharing are presented in [ 1 ], where protocols for intersection, equijoin, intersection size and equijoin size across private databases are discussed and analysed.”
Privacy-Preserving Record Linkage (2010, methodology): “As shown in [1], this naive method fails since the hash function is deterministic.”
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data (2008, methodology): “This idea is also used by [2] to compute Set Union, Set Intersection, Size of Set Union, and Size of Set Intersection.”
A taxonomy of privacy-preserving record linkage techniques (2013, methodology): “The two major cryptographic encryption schemes used for secure computation in the PPRL literature are commutative [96] and homomorphic [97] encryption.”
Privacy preservation in wireless sensor networks: A state-of-the-art survey (2009, background): “All rights reserved. hang10@gwu.edu (N. uraisingham@utdal- cerns may also arise beyond data content and may focus on context information such as the location of a sensor initiating data communication.”
Privacy preserving decision tree learning over multiple parties (2007, methodology): “Similar to [3] [17], Aggarwal et al. [2] propose a method for finding the kth ranked element approximately in the union of more than two databases using secure multi-party computation.”
L-diversity: Privacy beyond k-anonymity (2007, neutral): “[Agrawal et al. 2003] employ commutative encryption techniques for information sharing across private database.”
Phasing: Private Set Intersection Using Permutation-based Hashing (2015, methodology): “DH-based PSI: The DH-based PSI protocol is secure based on the Decisional Difﬁe-Hellmann (DDH) assumption (cf. security proof in [AES03]) and works as follows: • Both parties agree on a cyclic group of prime order q and on a hash function H that is modeled as a random oracle.”
Privacy preserving schema and data matching (2007, methodology): “We also used Java Cryptography Extension (available at http://java.sun.com/products/jce/) for implementing the exact private matching protocol described in Agrawal et al. [ 2 ].”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, background): “Several works limit the parties to learning only the cardinality of the intersection [25, 1, 33, 51, 14, 40, 30].”
Efficient k -Anonymization Using Clustering Techniques (2007, background): “There has also been much work done addressing privacy-preserving information sharing [32, 3], where the main concern is the privacy of databases rather than data subjects.”
Data Matching (2012, background): “Both set intersection and equijoin protocols are then presented, and their security and their communication and computation complexities are analysed [6].”
Fast and Private Computation of Cardinality of Set Intersection and Union (2012, methodology): “Agrawal, Evfiviemski, and Srikant [1] introduce a Private Set Intersection (PSI) construction based on commutative encryption.”
Privacy-Preserving Relationship Path Discovery in Social Networks (2009, background): “Private set intersection protocols [3, 8, 11] enable two or more parties that each hold a set of inputs drawn from a large domain to jointly calculate the intersection of their inputs, without leaking additional information.”