Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Randomization in privacy preserving data mining
ACM SIGKDD Explorations Newsletter
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
Privacy Preserving Data Classification with Rotation Perturbation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Privacy preservation for data cubes
Knowledge and Information Systems
Deriving Private Information from Perturbed Data Using IQR Based Approach
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
On the use of spectral filtering for privacy preserving data mining
Proceedings of the 2006 ACM symposium on Applied computing
Handicapping attacker's confidence: an alternative to k-anonymization
Knowledge and Information Systems
Deriving private information from arbitrarily projected data
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
An attacker's view of distance preserving maps for privacy preserving data mining
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Breaching Euclidean distance-preserving data perturbation using few known inputs
Data & Knowledge Engineering
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Additive randomization has been a primary tool for hiding sensitive private information. Previous work empirically showed that individual data values can be approximately reconstructed from the perturbed values, using spectral filtering techniques. This poses a serious threat of privacy breaches. In this paper we conduct a theoretical study on how the reconstruction error varies, for different types of additive noise. In particular, we first derive an upper bound for the reconstruction error using matrix perturbation theory. Attackers who use spectral filtering techniques to estimate the true data values may leverage this bound to determine how close their estimates are to the original data. We then derive a lower bound for the reconstruction error, which can help data owners decide how much noise should be added to satisfy a given threshold of the tolerated privacy breach.