Achieving k-Anonymity Privacy Protection Using Generalization and Suppression

  title={Achieving k-Anonymity Privacy Protection Using Generalization and Suppression},
  author={Latanya Sweeney},
  journal={Int. J. Uncertain. Fuzziness Knowl. Based Syst.},
  • L. Sweeney
  • Published 1 October 2002
  • Computer Science
  • Int. J. Uncertain. Fuzziness Knowl. Based Syst.
Often a data holder, such as a hospital or bank, needs to share person-specific records in such a way that the identities of the individuals who are the subjects of the data cannot be determined. [] Key Method Generalization involves replacing (or recoding) a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all.
Privacy-Preserving Distributed k-Anonymity
A key contribution is a proof that the protocol preserves k-anonymity between the sites, a fundamentally different distributed privacy definition than that of Secure Multiparty Computation, and it provides a better match with both ethical and legal views of privacy.
Privacy-enhancing k-anonymization of customer data
This paper provides privacy-enhancing methods for creating k-anonymous tables in a distributed scenario in such a way that does not reveal any extra information that can be used to link sensitive attributes to corresponding identifiers, and without requiring a central authority who has access to all the original data.
kACTUS 2: Privacy Preserving in Classification Tasks Using k-Anonymity
This work investigates data anonymization in the context of classification and use tree properties to satisfy k-anonymization and presents a hybrid approach called compensation which is based on suppression and swapping for achieving privacy.
Privacy Preservation Measure using t-closeness with combined l-diversity and k-anonymity
This work proposes a unique method by combining two of the most widely used privacy preservation techniques: K-anonymity and l-diversity, and presents a new notion of privacy called “closeness”.
Classification Tree-Based k-Anonymity with Masking Operations to Enhance Data Utility
An approach to achieve kanonymity named as Classification Tree-based K-Anonymity using Masking operations (CTKAM), which is efficient for handling both suppression and generalization operation and Experimental results show CTKAM is able to handle both generalization and suppression operations.
Hybrid k-Anonymity
Methods for privacy protection using k-anonymity
  • V. Sharma
  • Computer Science
    2014 International Conference on Reliability Optimization and Information Technology (ICROIT)
  • 2014
This method provides a guarantee that released data is at least k-anonymous, so that it may not harm the privacy of individuals and sensitive information from being released.
Data privacy preservation algorithm with k-anonymity
The proposed algorithm could effectively preserve data privacy and also reduce the number of visited nodes for ensuring the privacy protection, which is the most time-consuming process, compared to the most efficient existing algorithm by at most 21%.
Achieving P-Sensitive K-Anonymity via Anatomy
A novel permutation-based approach called \textit{anatomy} to release the quasi-identifier and sensitive values directly in two separate tables that protect privacy, but captures a large amount of correlation in the microdata.
Anonymity : Formalisation of Privacy – k-anonymity
It is shown, how l-diversity and t-closeness provide a stronger level of anonymity as k-anonymity, and a value generalization hierarchy based on the attributes model, device, version and network is provided.


k-Anonymity: A Model for Protecting Privacy
  • L. Sweeney
  • Computer Science
    Int. J. Uncertain. Fuzziness Knowl. Based Syst.
  • 2002
The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Confidentiality, Disclosure and Data Access: Theory and Practical Applications for Statistical Agencies
Disclosure limitation methods in use - results of a survey, F. Felso, J. Theeuwes, G.G. Wagner Information Explosion, L. Sweeney Disclosure risk assessment, M. Elliot Disclosure control methods and
Computational disclosure control: a primer on data privacy protection
Principles of Database and Knowledge-Base Systems, Volume II
  • J. Ullman
  • Computer Science
    Principles of computer science series
  • 1988
Guaranteeing anonymity when sharing medical data, the Datafly System
We present a computer program named Datafly that maintains anonymity in medical data by automatically generalizing, substituting, and removing information as appropriate without losing many of the
i- and T-argus: software for statistical disclosure control
  • Third International Seminar on Statistical Confidentiality
  • 1996
Vicenc Torra and Josep Domingo provided the opportunity to write this paper
  • Vicenc Torra and Josep Domingo provided the opportunity to write this paper
Finding a needle in a haystack - or dentifying anonymous census record
  • Journal of Official Statistics,
  • 1986
Information Explosion. Confidentiality, Disclosure, and Data Access: Theory and Practical Applications for Statistical Agencies, L
  • 2001