Privacy Preserving Data mining techniques depends on privacy, which captures what information is sensitive in the original data and should therefore be protected from either direct or indirect disclosure. Secrecy and anonymity are useful ways of thinking about privacy. This privacy should be measureable and entity to be considered private should be valuable. In this paper, we discuss the variou...

Aiming at ensuring privacy preservation in personal data publishing, the topic of anonymization has been intensively studied in recent years. However, existing anonymization techniques all assume each tuple in the microdata table contains one single sensitive attribute (the SSA case), while none paid attention to the case of multiple sensitive attributes in a tuple (the MSA case). In this paper...

Consider a database D with records containing history of individuals’ transactions, that has been de-identified, i.e., the variables that uniquely associate records with individuals have been removed from the data. An adversary de-anonymizes D via a linkage attack if using some auxiliary information about a certain individual in the database, it can determine which record of D corresponds to su...

Before sharing to support ad hoc aggregate analyses, microdata often need to be anonymized to protect the privacy of individuals. A variety of privacy models have been proposed for microdata anonymization. Many of these models (e.g., -closeness) essentially require that, after anonymization, groups of sensitive attribute values follow specified distributions. To support such models, in this pap...

In an effort to promote our research in the area of privacy preserving data analysis, as UT Dallas Data Security and Privacy Lab, we complied our implementation of various anonymization methods into a toolbox for public use by researchers. This document explains basic information on how to invoke these anonymization methods (section 1), various features of our implementation of the methods (sec...

Privacy Preserving Data Publishing refers publishing data in such a way that the privacy of the individuals are preserved. The Published data can further be used for various Data Analysis and Data Mining tasks. Techniques used to preserve privacy of individuals before publishing is called Anonymization Techniques. Initially only centralized data need to be published for analysis and Mining. Lat...

For publishing sign language corpus data on the web, anonymization is crucial even if it is impossible to hide the visual appearance of the signers: In a small community, even vague references to third persons may be enough to identify those persons. In the case of the DGS Korpus (German Sign Language corpus) project, we want to publish data as a contribution to the cultural heritage of the sig...

Personal information anonymity concerns anonymizing information that identifies individuals, in contrast to anonymizing activities such as downloading copyrighted items on the Internet. It may refer to encrypting personal data, generalization and suppression as in k-anonymization, ‘untraceability’ or ‘unidentifiability’ of identity in the network, etc. A common notion is hiding the “identities”...

Private data such as electronic health records and banking transactions must be shared within the cloud environment to analysis or mine data for research purposes. Data privacy is one of the most concerned issues in big data applications, because processing large-scale sensitive data sets often requires computation power provided by public cloud services. A technique called Data Anonymization, ...

Hospitals and health care organizations collect large amounts of detailed health care data that is in high demand by researchers. Thus, the possessors of such data are in need of methods that allow for this data to be released without compromising the confidentiality of the individuals to whom it pertains. As the geographic aspect of this data is becoming increasingly relevant for research bein...