An access control model supporting periodicity constraints and temporal reasoning
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Expected time bounds for selection
Communications of the ACM
Secure and selective dissemination of XML documents
ACM Transactions on Information and System Security (TISSEC)
Some facets of complexity theory and cryptography: A five-lecture tutorial
ACM Computing Surveys (CSUR)
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and 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 the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Practical privacy: the SuLQ framework
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
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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
Anonymizing sequential releases
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
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Towards robustness in query auditing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Approximate algorithms for K-anonymity
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '02 Proceedings of the 28th 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
ICDT'05 Proceedings of the 10th international conference on Database Theory
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Hiding distinguished ones into crowd: privacy-preserving publishing data with outliers
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
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)
P-Sensitive K-Anonymity with Generalization Constraints
Transactions on Data Privacy
APPT: A privacy preserving transformation tool for micro data release
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Generalizing PIR for practical private retrieval of public data
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
On-the-fly hierarchies for numerical attributes in data anonymization
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
Preventing range disclosure in k-anonymised data
Expert Systems with Applications: An International Journal
Probabilistic inverse ranking queries in uncertain databases
The VLDB Journal — The International Journal on Very Large Data Bases
SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness
The VLDB Journal — The International Journal on Very Large Data Bases
On-the-fly generalization hierarchies for numerical attributes revisited
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Satisfying privacy requirements: one step before anonymization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
The hardness of (ε, m)-anonymity
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
A general framework for privacy preserving data publishing
Knowledge-Based Systems
MAGE: A semantics retaining K-anonymization method for mixed data
Knowledge-Based Systems
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We identify proximity breach as a privacy threat specific to numerical sensitive attributes in anonymized data publication. Such breach occurs when an adversary concludes with high confidence that the sensitive value of a victim individual must fall in a short interval --- even though the adversary may have low confidence about the victim's actual value. None of the existing anonymization principles (e.g., k-anonymity, l-diversity, etc.) can effectively prevent proximity breach. We remedy the problem by introducing a novel principle called (ε, m)-anonymity. Intuitively, the principle demands that, given a QI-group G, for every sensitive value x in G, at most 1/m of the tuples in G can have sensitive values "similar" to x, where the similarity is controlled by ε. We provide a careful analytical study of the theoretical characteristics of (ε, m)-anonymity, and the corresponding generalization algorithm. Our findings are verified by experiments with real data.