Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Template-Based Privacy Preservation in Classification Problems
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The cost of privacy: destruction of data-mining utility in anonymized data publishing
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Publishing Sensitive Transactions for Itemset Utility
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Expressing privacy metrics as one-symbol information
Proceedings of the 2010 EDBT/ICDT Workshops
Versatile publishing for privacy preservation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Rights protection of trajectory datasets with nearest-neighbor preservation
The VLDB Journal — The International Journal on Very Large Data Bases
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
Utility-oriented K-anonymization on social networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
An information theoretic approach for privacy metrics
Transactions on Data Privacy
Transactions on Data Privacy
Third party geolocation services in LBS: privacy requirements and research issues
Transactions on Data Privacy
Testing software in age of data privacy: a balancing act
Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering
Protecting privacy in data release
Foundations of security analysis and design VI
Anonymization of location data does not work: a large-scale measurement study
MobiCom '11 Proceedings of the 17th annual international conference on Mobile computing and networking
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
Detecting and resolving privacy conflicts for collaborative data sharing in online social networks
Proceedings of the 27th Annual Computer Security Applications Conference
On t-closeness with KL-divergence and semantic privacy
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Hiding emerging patterns with local recoding generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Fast track article: Balancing behavioral privacy and information utility in sensory data flows
Pervasive and Mobile Computing
Information based data anonymization for classification utility
Data & Knowledge Engineering
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment
On location privacy and quality of information in participatory sensing
Proceedings of the 8h ACM symposium on QoS and security for wireless and mobile networks
Efficient discovery of de-identification policy options through a risk-utility frontier
Proceedings of the third ACM conference on Data and application security and privacy
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
Anonymizing classification data using rough set theory
Knowledge-Based Systems
Trends and research directions for privacy preserving approaches on the cloud
Proceedings of the 6th ACM India Computing Convention
Journal of Computer Security
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In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility of the data. It is important to consider the tradeoff between privacy and utility. In a paper that appeared in KDD 2008, Brickell and Shmatikov proposed an evaluation methodology by comparing privacy gain with utility gain resulted from anonymizing the data, and concluded that "even modest privacy gains require almost complete destruction of the data-mining utility". This conclusion seems to undermine existing work on data anonymization. In this paper, we analyze the fundamental characteristics of privacy and utility, and show that it is inappropriate to directly compare privacy with utility. We then observe that the privacy-utility tradeoff in data publishing is similar to the risk-return tradeoff in financial investment, and propose an integrated framework for considering privacy-utility tradeoff, borrowing concepts from the Modern Portfolio Theory for financial investment. Finally, we evaluate our methodology on the Adult dataset from the UCI machine learning repository. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.