Secure databases

By David P. Dobkin, Anita K. Jones, Richard Jay Lipton. 1979

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Citations in the corpus

On the Difficulties of Disclosure Prevention in Statistical Databases or The Case for Differential Privacy (2010, background): “Journal of Privacy and Confidentiality (2010) 2, Number 1, pp. 93–107”
Differential Privacy (2006, unknown): “Research in the theoretical computer science community in the late 1970’s had very specific definitions of privacy compromise, or what the adversary must achieve to be considered successful (see, eg, [9]).”
An Ad Omnia Approach to Defining and Achieving Private Data Analysis (2007, background): “There is a rich literature on this problem, principally from the satistics community [11, 15, 24, 25, 26, 34, 36, 23, 35] (see also the literature on controlled release of tabular data, contingency tables, and cell suppression), and from such diverse branches of computer science as algorithms, datab”
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms (2008, background): “There are many flavors of query restriction, like restricting the number of entities in the result set [25], controlling the overlap among successive queries [26], keeping up-to-date logs of all queries and checking for compromises whenever a new query is issued [27], and clustering individual entit”
Efficient k -Anonymization Using Clustering Techniques (2007, background): “Another important technique is query restriction [7, 9], which restricts queries that may result in inference.”
Hippocratic Databases (2002, background): “The query restriction family includes restricting the size of query results [13] [18], controlling the overlap among successive queries [14], keeping audit trails of all answered queries and constantly checking for possible compromises [8], suppression of data cells of small size [9], and…”
L-diversity: privacy beyond k-anonymity (2006, unknown): “The query set overlap control technique [ Dobkin et al. 1979 ] disallows queries which have a large intersection with the previous queries.”
Two Can Keep A Secret: A Distributed Architecture for Secure Database Services (2005, unknown): “Privacy-preserving Data Mining Dieren t approaches for privacy-preserving data mining studied recently include: (1) perturbation techniques [AS00, AA01, EGS03, DN03, DN04] (2) query restriction/auditing [CO82, DJL79 , KPR00] (3) k-anonymity [Swe02, MW04, AFK+04].”
Revealing information while preserving privacy (2003, background): “To solve this problem one may try to restrict query overlap [11], but again database privacy is compromised, unless the number of queries is very low.”
Information sharing across private databases (2003, background): “These techniques include restricting the size of query results [17, 23], controlling the overlap among successive queries [19], and keeping audit trails of all answered queries to detect possible compromises [13].”
Differential Privacy: A Survey of Results (2008, unknown): “release of tabular data, contingency tables, and cell suppression), and from such diverse branches of computer science as algorithms, database theory, and cryptography, for example as in [3, 4, 21, 22, 23, 33, 34, 41, 48], [1, 24, 25, 31], and [6, 9, 11, 12, 13, 18, 7, 19]; see also the survey [2] f”
L-diversity: Privacy beyond k-anonymity (2007, unknown): “The query monitoring approach [Dobkin et al. 1979; Chin 1986] is an online version of the problem where the (t+1) query is answered or not depending on the first t queries asked.”