Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Security in database systems: a research perspective
Computers and Security
A modified random perturbation method for database security
ACM Transactions on Database Systems (TODS)
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Assessing global disclosure risk in masked microdata
Proceedings of the 2004 ACM workshop on Privacy in the electronic society
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
To do or not to do: the dilemma of disclosing anonymized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards the Diversity of Sensitive Attributes in k-Anonymity
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
An epistemic framework for privacy protection in database linking
Data & Knowledge Engineering
A privacy-preserving index for range queries
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On-line data protecting via pseudo random binary sequences
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
A novel data distortion approach via selective SSVD for privacy protection
International Journal of Information and Computer Security
Dynamic anonymization: accurate statistical analysis with privacy preservation
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Protecting privacy in recorded conversations
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge
ACM Transactions on Knowledge Discovery from Data (TKDD)
Disclosure Risks of Distance Preserving Data Transformations
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Evaluating privacy threats in released database views by symmetric indistinguishability
Journal of Computer Security - Selected papers from the Third and Fourth Secure Data Management (SDM) workshops
Privacy preserving churn prediction
Proceedings of the 2009 ACM symposium on Applied Computing
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Maximizing Privacy under Data Distortion Constraints in Noise Perturbation Methods
Privacy, Security, and Trust in KDD
A distributed approach to enabling privacy-preserving model-based classifier training
Knowledge and Information Systems
Publishing naive Bayesian classifiers: privacy without accuracy loss
Proceedings of the VLDB Endowment
Preventing interval-based inference by random data perturbation
PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies
Granulation as a privacy protection mechanism
Transactions on rough sets VII
IEEE Transactions on Information Technology in Biomedicine
Software—Practice & Experience - Focus on Selected PhD Literature Reviews in the Practical Aspects of Software Technology
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
Privacy preservation by independent component analysis and variance control
Proceedings of the 20th ACM international conference on Information and knowledge management
Information theory and the security of binary data perturbation
INDOCRYPT'04 Proceedings of the 5th international conference on Cryptology in India
Indistinguishability: the other aspect of privacy
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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Statistical databases often use random data perturbation (RDP) methods to protect against disclosure of confidential numerical attributes. One of the key requirements of RDP methods is that they provide the appropriate level of security against snoopers who attempt to obtain information on confidential attributes through statistical inference. In this study, we evaluate the security provided by three methods of perturbation. The results of this study allow the database administrator to select the most effective RDP method that assures adequate protection against disclosure of confidential information.