Achieving K Anonymity Privacy Protection Using Generalization And Suppression Pdf
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- Reducing the disclosure risk
- Achieving k-Anonymity Privacy Protection Using Generalization and Suppression
- A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points
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Reducing the disclosure risk
Objective: There is increasing pressure to share health information and even make it publicly available. However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets. Design: Through a simulation, we evaluated the re-identification risk of k-anonymization and three different improvements on three large data sets.
Achieving k-Anonymity Privacy Protection Using Generalization and Suppression
Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k -anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.
A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points
This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. Generalization involves replacing or recoding a value with a less specific but semantically consistent value. Suppression involves not releasing a value at all. The Preferred Minimal Generalization Algorithm MinGen , which is a theoretical algorithm presented herein, combines these techniques to provide k-anonymity protection with minimal distortion.
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The concept of k -anonymity was first introduced by Latanya Sweeney and Pierangela Samarati in a paper published in  as an attempt to solve the problem: "Given person-specific field-structured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re-identified while the data remain practically useful. In the context of k -anonymization problems, a database is a table with n rows and m columns. Each row of the table represents a record relating to a specific member of a population and the entries in the various rows need not be unique.
International Journal of Computer Applications 90 15 , March Full text available. Nowadays, in given network or system we have to share personal specific data.
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Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Fuzziness Knowl. Based Syst. Sweeney Published Computer Science Int. 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.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. A New Method of Privacy Protection: Random k-Anonymous Abstract: A new k-anonymous method which is different from traditional k-anonymous was proposed to solve the problem of privacy protection. Specifically, numerical data achieves k-anonymous by adding noises, and categorical data achieves k-anonymous by using randomization. Using the above two methods, the drawback that at least k elements must have the same quasi identifier in the k-anonymous data set has been solved.
Achieving K-Anonymity Privacy Protection Using Generalization and Suppression (2002)
Authors: Sabah S. Keywords: Balanced tables , k-anonymization , private data. Commenced in January Frequency: Monthly. Edition: International. Paper Count:
Sharing microdata tables is a primary concern in today information society. Privacy issues can be an obstacle to the free flow of such information. In recent years, disclosure control techniques have been developed to modify microdata tables in order to be anonymous. The k -anonymity framework has been widely adopted as a standard technique to remove links between public available identifiers such as full names and sensitive data contained in the shared tables. In this paper we give a weaker definition of k -anonymity, allowing lower distortion on the anonymized data.
The first obvious application of this method is the removal of direct identifiers from the data file. A variable should be removed when it is highly identifying and no other protection methods can be applied. A variable can also be removed when it is too sensitive for public use or irrelevant for analytical purpose. For example, information on race, religion, HIV, etc. Removing records can be used as an extreme measure of data protection when the unit is identifiable in spite of the application of other protection techniques.
Published on Authors of this article:. However, k-anonymity cannot prevent sensitive attribute disclosure. An alternative, l -diversity, has been proposed as a solution to this problem and is defined as: each Q-block ie, each set of rows corresponding to the same value for identifiers contains at least l well-represented values for each sensitive attribute.
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