Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
A maximum entropy approach to natural language processing
Computational Linguistics
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
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Efficient Stepwise Selection in Decomposable Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
The Complexity of Three-Way Statistical Tables
SIAM Journal on Computing
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Toward privacy in public databases
TCC'05 Proceedings of the Second international conference on Theory of Cryptography
Anonymizing sequential releases
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
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Data & Knowledge Engineering
Information disclosure under realistic assumptions: privacy versus optimality
Proceedings of the 14th ACM conference on Computer and communications security
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
The boundary between privacy and utility in data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
K-anonymization as spatial indexing: toward scalable and incremental anonymization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A framework for condensation-based anonymization of string data
Data Mining and Knowledge Discovery
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
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
Does enforcing anonymity mean decreasing data usefulness?
Proceedings of the 4th ACM workshop on Quality of protection
Towards privacy-preserving integration of distributed heterogeneous data
Proceedings of the 2nd PhD workshop on Information and knowledge management
Detecting privacy violations in database publishing using disjoint queries
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Adversarial-knowledge dimensions in data privacy
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving data publishing for cluster analysis
Data & Knowledge Engineering
Anonymization-based attacks in privacy-preserving data publishing
ACM Transactions on Database Systems (TODS)
A framework for efficient data anonymization under privacy and accuracy constraints
ACM Transactions on Database Systems (TODS)
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Attacks on privacy and deFinetti's theorem
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A novel anonymization algorithm: Privacy protection and knowledge preservation
Expert Systems with Applications: An International Journal
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
An integrated framework for de-identifying unstructured medical data
Data & Knowledge Engineering
Distortion-based anonymity for continuous queries in location-based mobile services
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Optimal random perturbation at multiple privacy levels
Proceedings of the VLDB Endowment
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Transparent anonymization: Thwarting adversaries who know the algorithm
ACM Transactions on Database Systems (TODS)
The hardness and approximation algorithms for l-diversity
Proceedings of the 13th International Conference on Extending Database Technology
Algorithm-safe privacy-preserving data publishing
Proceedings of the 13th International Conference on Extending Database Technology
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
A practice-oriented framework for measuring privacy and utility in data sanitization systems
Proceedings of the 2010 EDBT/ICDT Workshops
Towards publishing recommendation data with predictive anonymization
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Capture inference attacks for K-anonymity with privacy inference logic
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Privacy inference attacking and prevention on multiple relative k-anonymized microdata sets
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Allowing privacy protection algorithms to jump out of local optimums: an ordered greed framework
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Website privacy preservation for query log publishing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Privacy-preserving data mining through knowledge model sharing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Efficient Anonymizations with Enhanced Utility
Transactions on Data Privacy
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
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Synthesizing: art of anonymization
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
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
Fragments and loose associations: respecting privacy in data publishing
Proceedings of the VLDB Endowment
ACM Transactions on Database Systems (TODS)
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
Privacy-preserving data sharing in cloud computing
Journal of Computer Science and Technology
Discord region based analysis to improve data utility of privately published time series
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Can the Utility of Anonymized Data be Used for Privacy Breaches?
ACM Transactions on Knowledge Discovery from Data (TKDD)
Dynamic anonymization for marginal publication
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
A publication process model to enable privacy-aware data sharing
IBM Journal of Research and Development
Query evaluation on a database given by a random graph
ICDT'07 Proceedings of the 11th international conference on Database Theory
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
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
k-Concealment: An Alternative Model of k-Type Anonymity
Transactions on Data Privacy
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
An automated data utility clustering methodology using data constraint rules
Proceedings of the 2012 international workshop on Smart health and wellbeing
Generically extending anonymization algorithms to deal with successive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
An Enhanced Utility-Driven Data Anonymization Method
Transactions on Data Privacy
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
Anonymizing sequential releases under arbitrary updates
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Negotiation-based privacy preservation scheme in internet of things platform
Proceedings of the First International Conference on Security of Internet of Things
Extending loose associations to multiple fragments
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
Efficient Time-Stamped Event Sequence Anonymization
ACM Transactions on the Web (TWEB)
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Limiting disclosure in data publishing requires a careful balance between privacy and utility. Information about individuals must not be revealed, but a dataset should still be useful for studying the characteristics of a population. Privacy requirements such as k-anonymity and l-diversity are designed to thwart attacks that attempt to identify individuals in the data and to discover their sensitive information. On the other hand, the utility of such data has not been well-studied.In this paper we will discuss the shortcomings of current heuristic approaches to measuring utility and we will introduce a formal approach to measuring utility. Armed with this utility metric, we will show how to inject additional information into k-anonymous and l-diverse tables. This information has an intuitive semantic meaning, it increases the utility beyond what is possible in the original k-anonymity and l-diversity frameworks, and it maintains the privacy guarantees of k-anonymity and l-diversity.