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- Reducing the disclosure risk
- A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points
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Reducing the disclosure risk
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. We show that, under the hypothesis in which the adversary is not sure a priori about the presence of a person in the table, the privacy properties of k -anonymity are respected also in the weak k -anonymity framework. Experiments on real-world data show that our approach outperforms k -anonymity in terms of distortion introduced in the released data by the algorithms to enforce anonymity.
A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points
In data sharing privacy has become one of the main concerns particularly when sharing datasets involving individuals contain private sensitive information. A model that is widely used to protect the privacy of individuals in publishing micro-data is k-anonymity. It reduces the linking confidence between private sensitive information and specific individual by generalizing the identifier attributes of each individual into at least k-1 others in dataset. K-anonymity can also be defined as clustering with constrain of minimum k tuples in each group. However, the accuracy of the data in k-anonymous dataset decreases due to huge information loss through generalization and suppression.
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. The real-world algorithms Datafly and -Argus are compared to MinGen. Both Datafly and -Argus use heuristics to make approximations, and so, they do not always yield optimal results. It is shown that Datafly can over distort data and -Argus can additionally fail to provide adequate protection.
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.
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Authors: Sabah S. Keywords: Balanced tables , k-anonymization , private data. Commenced in January Frequency: Monthly.
Published on Authors of this article:. However, k-anonymity cannot prevent sensitive attribute disclosure.
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.
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.
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.
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. The values in the various columns are the values of attributes associated with the members of the population.
Все было совсем не .
Беккер безучастно кивнул: - Так мне сказали. Лейтенант вздохнул и сочувственно помотал головой. - Севильское солнце бывает безжалостным. Будьте завтра поосторожнее. - Спасибо, - сказал Беккер.
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