SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets

Poulis, Giorgos; Gkoulalas-Divanis, Aris; Loukides, Grigorios; Skiadopoulos, Spiros; Tryfonopoulos, Christos

doi:10.1007/978-3-319-23633-9_5

Cited by 15 publications

(15 citation statements)

References 18 publications

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“…Randomized, or stochastic, privacy-preserving policies have been shown to cause problems, such as un-truthfulness [33], which can be undesirable in practice [34]. This is perhaps one of the reason behind low popularity of randomized privacy-2 Popularity of these methods is somehwat evident from the sheer number of available toolboxes for implementation https://arx.deidentifier.org/overview/related-software/ preserving policies, such as differential privacy, within the financial or health sectors [33].…”

Section: Introductionmentioning

confidence: 99%

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

Farokhi

2019

IEEE Trans.Inform.Forensic Secur.

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A deterministic privacy metric using non-stochastic information theory is developed. Particularly, maximin information is used to construct a measure of information leakage, which is inversely proportional to the measure of privacy. Anyone can submit a query to a trusted agent with access to a non-stochastic uncertain private dataset. Optimal deterministic privacy-preserving policies for responding to the submitted query are computed by maximizing the measure of privacy subject to a constraint on the worst-case quality of the response (i.e., the worst-case difference between the response by the agent and the output of the query computed on the private dataset). The optimal privacy-preserving policy is proved to be a piecewise constant function in the form of a quantization operator applied on the output of the submitted query. The measure of privacy is also used to analyze k-anonymity (a popular deterministic mechanism for privacy-preserving release of datasets using suppression and generalization techniques), proving that it is in fact not privacypreserving.

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Section: Introductionmentioning

confidence: 99%

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

Farokhi

2019

IEEE Trans.Inform.Forensic Secur.

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“…• Subtree generalization: In this method, if a value of an attribute is generalized to its parent node, all other child nodes of that parent node need to be replaced with the parent node as well [17], [18].…”

Section: Generalization Techniquementioning

confidence: 99%

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Shaham

Ding

Liu

et al. 2021

IEEE Trans. Knowl. Data Eng.

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Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

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“…Second, we plan to improve the utility of output data in high‐dimensional settings by implementing methods to better handle complex inter‐attribute relationships . One possible solution to this problem is to treat the data as transactional, that is, set‐valued, and to employ specific privacy models, such as k m ‐anonymity, which is implemented by Anamnesia and SECRETA …”

Section: Limitations and Challenges Aheadmentioning

confidence: 99%

“…The current landscape of open source anonymization software basically consists of three types of solutions: First, there are tools originating from the computer science community (typically research prototypes), such as the UTD Anonymization Toolbox , the Cornell Anonymization Toolkit , TIAMAT , Anamnesia or SECRETA and source code published as supplementary material to articles (eg, References and ). These solutions are able to automatically enforce privacy guarantees specified by users a priori.…”

Section: Introductionmentioning

confidence: 99%

Flexible data anonymization using ARX—Current status and challenges ahead

et al. 2020

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The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk-reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality-while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.

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SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets

Cited by 15 publications

References 18 publications

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

Development and Analysis of Deterministic Privacy-Preserving Policies Using Non- Stochastic Information Theory

Privacy Preserving Location Data Publishing: A Machine Learning Approach

Flexible data anonymization using ARX—Current status and challenges ahead

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