Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cell Suppression to Limit Content-Based Disclosure
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information System Track-Organizational Systems and Technology - Volume 3
Disclosure Limitation through Additive Noise Data Masking: Analysis of Skewed Sensitive Data
HICSS '97 Proceedings of the 30th Hawaii International Conference on System Sciences: Information System Track-Organizational Systems and Technology - Volume 3
On the value of private information
TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Analysis of privacy preserving random perturbation techniques: further explorations
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Tools for privacy preserving Kernel methods in data mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Enabling the 21st century health care information technology revolution
Communications of the ACM - Spam and the ongoing battle for the inbox
Hiding informative association rule sets
Expert Systems with Applications: An International Journal
Data Mining and Knowledge Discovery
IEEE Pervasive Computing
Hiding collaborative recommendation association rules
Applied Intelligence
SenseWorld: Towards Cyber-Physical Social Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Efficient sanitization of informative association rules
Expert Systems with Applications: An International Journal
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
PoolView: stream privacy for grassroots participatory sensing
Proceedings of the 6th ACM conference on Embedded network sensor systems
Maintenance of sanitizing informative association rules
Expert Systems with Applications: An International Journal
Knowledge and Information Systems
Hiding Predictive Association Rules on Horizontally Distributed Data
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
International Journal of Data Analysis Techniques and Strategies
Hiding collaborative recommendation association rules on horizontally partitioned data
Intelligent Data Analysis
Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters
Transactions on Data Privacy
A three-dimensional conceptual framework for database privacy
SDM'07 Proceedings of the 4th VLDB conference on Secure data management
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Privacy-aware regression modeling of participatory sensing data
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Privacy preserving protocols for eigenvector computation
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Information driven evaluation of data hiding algorithms
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Providing group anonymity using wavelet transform
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Efficient Protocols for Principal Eigenvector Computation over Private Data
Transactions on Data Privacy
Theoretical Results on De-Anonymization via Linkage Attacks
Transactions on Data Privacy
An Enhanced Utility-Driven Data Anonymization Method
Transactions on Data Privacy
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it with a randomization algorithm and then submitting the randomized version. The randomization algorithm is chosen so that aggregate properties of the data can be recovered with sufficient precision, while individual entries are significantly distorted. How much distortion is needed to protect privacy can be determined using a privacy measure. Several possible privacy measures are known; finding the best measure is an open question. This paper presents some methods and results in randomization for numerical and categorical data, and discusses the issue of measuring privacy.