A tutorial josep domingoferrer universitat rovira i virgili, tarragona, catalonia josep. Data anonymization techniques methods kanonymity is a requirement that pseudoidentifiers of each record must match at least k other records in the anonymized dataset. If the data is in public domain then it is a threat to individual privacy as the data is held by data holder. So far, the data anonymization approaches based on kanonymity and ldiversity has contributed much to privacy protection from record and attributes linkage attacks. Sweeney presents k anonymity as a model for protecting privacy. A popular approach for data anonymization is kanonymity. The problem of the optimal k anonymization is nphard. An important requirement for such techniques is to ensure anonymization of data while at the.
Towards optimal k anonymization tiancheng li ninghui li cerias and department of computer science, purdue university 305 n. To multidimensional data add one more attribute data provider, which can be used as any other attribute in anonymization. New privacy regulation, most notably the gdpr, are making it increasingly difficult to maintain a balance between privacy and utility. However, the existing solutions are not efficient when applied to multimedia big data anonymization. Such techniques reduce risk and assist data processors in fulfilling their data compliance regulations. A common practice for the privacy preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k anonymity. Unlike traditional privacy protection techniques such as data swapping and adding noise, information in a kanonymized table through generalization and suppression remains truthful. Given personspecific fieldstructured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot be re. In this paper, we propose a greedybased heuristic approach that provides an optimal value for k.
Specifically, we consider a setting in which there is a set of. Anonymization software and bibliography data formats tabular data. This paper investigates the basic tabular structures that. Our solutions enhance the privacy of kanonymization in the distributed scenario by maintaining endtoend privacy from the original customer data to the. To keep on top of data anonymization, tech firms and dataheavy organizations are looking to hire professionally trained business data analytics personnel. Jan 09, 2008 a popular approach for data anonymization is kanonymity. Data privacy through optimal kanonymization proceedings. An electronic trail is the information that is left behind when someone sends data over a network. Destruction of datamining utility in anonymized data publishing.
In this paper, we provide privacy enhancing methods for creating k anonymous tables in a distributed scenario. University street, west lafayette, in 479072107, usa. Data holder can be social networking application, websites, mobile apps, ecommerce site, banks, hospitals etc. Efficient k anonymization using clustering techniques. An additional line of research, which we will call data anonymization, has formulated syntactic.
In order to protect individuals privacy, the technique of kanonymization has been proposed to deassociate sensitive attributes from the corresponding identifiers. In a kanonymous dataset, any identifying information occurs in at least k. Justified privacy concerns exist for all research data whose generation involves the collection of personal data. The proposed algorithms determine an optimal solution based on the characteristics of the igh data by visiting and evaluating only essential nodes of generalization lattice that satisfy the k anonymity. Achieving optimal kanonymity parameters for big data. A reverse data mining technique that reidentifies encrypted or generalized information. Page 3 unless otherwise stated, the term data refers to personspecific information that is conceptually organized as a table of rows or records and. Sep 22, 2018 if the data is in public domain then it is a threat to individual privacy as the data is held by data holder. What formal privacy guarantee if any does kanonymization methods provide. University street, west lafayette, in 479072107, usa abstract when releasing microdata for research purposes, one needs to preserve the privacy of respondents while maximizing data utility. Towards optimal kanonymization tiancheng li ninghui li cerias and department of computer science, purdue university 305 n. Unfortunately, there is no any standard procedure to define the value of k.
So far, the data anonymization approaches based on k anonymity and ldiversity has contributed much to privacy protection from record and attributes linkage attacks. Among the arsenal of it security techniques available, pseudonymization or anonymization is highly recommended by the gdpr regulation. In a k anonymized dataset, each record is indistinguishable from at least k. They outline the role of five levers which help capture the value through the use of open and proprietary data. Arx a comprehensive tool for anonymizing biomedical data. Data masking is the standard solution for data pseudonymization. Although anonymization is an important method for privacy protection, there is a lack of tools which are both comprehensive and readily available to informatics. On the one hand, scientific research should be fostered by storing and interconnecting data, but on the other hand legal regulations prescribe the deletion of personal data after achieving the purpose of a research project at least in germany. An approach that has been studied extensively in recent years is to use anonymization techniques such as gen eralization and suppression to ensure that the released data table satisfies the k anonymity property. Introduction in todays information society, given the unprecedented ease of. International journal on uncertainty, fuzziness and knowledgebased systems,10 5, 2002.
The technique of kanonymization has been proposed to obfuscate private data through associating it with at least k identities. Publishing raw electronic health records ehrs may be considered as a breach of the privacy of individuals because they usually contain sensitive information. Data anonymization is the process of destroying tracks, or the electronic trail, on the data that would lead an eavesdropper to its origins. This paper proposes and evaluates an optimization algorithm for the powerful deidentification procedure known as kanonymization. The proposed algorithms determine an optimal solution based on the characteristics of the igh data by visiting and evaluating only essential nodes of generalization. On sampling, anonymization, and differential privacy. The technique of kanonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. Achieving k anonymity privacy protection using generalization and suppression. This paper proposes and evaluates an optimization algorithm for the powerful deidentification procedure known as anonymization. Data privacy through optimal kanonymization abstract. This paper investigates the basic tabular structures that underline the notion of k anonymization using cell suppression. Finding an optimal anonymization is not easy nphard. Data deidentification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. If it can be proven that the true identity of the individual cannot be derived from anonymized data, then this data is exempt.
Data holder can be social networking application, websites, mobile apps. The value of k should be carefully determined, to compromise both security and information gained. Efficient multimedia big data anonymization springerlink. The proposed algorithms determine an optimal solution based on the characteristics of the igh data by visiting and evaluating only essential nodes of generalization lattice that satisfy the kanonymity. Optimal kanonymization is an nphard problem and there are various approaches to meet this requirement. In order to protect individuals privacy, the technique of k anonymization has been proposed to deassociate sensitive attributes from the corresponding identifiers. Forensic experts can follow the data to figure out who sent it. Pdf enhancing privacy of confidential data using k. Data privacy through optimal kanonymization proceedings of. Algorithms that are suitable for use in practice typically employ greedy methods 6, or incomplete stochastic search 5,16, and do not provide any guarantees on the quality of the result. Tabular data protection queryable database protection microdata protection evaluation of sdc methods anonymization software and bibliography data anonymization. Achieving kanonymity privacy protection using generalization. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. Through experiments on real census data, we show the resulting algorithm can find optimal k anonymizations under two representative cost measures and a wide range of k.
Among various anonymization techniques, generalization is the most commonly. A tutorial josep domingoferrer universitat rovira i virgili, tarragona, catalonia. Data anonymization reduces the risk of unintended disclosure when sharing data between countries, industries, and even departments within the same company, explains on its data anonymization page. Although anonymization is an important method for privacy protection, there is a lack of tools which are both comprehensive and readily available to informatics researchers and also to nonit experts, e.
Kanonymization is not a good method to anonymize highdimensional datasets. This post walks the reader through a realworld example of a linkage attack to demonstrate the limits of data anonymization. This paper proposes and evaluates an optimization algorithm for the powerful deidentification procedure known as k anonymization. Classification and analysis of anonymization techniques for. Using masking, data can be deidentified and desensitized so that personal information remains anonymous in the context of. Deidentifying data through common formulations of anonymity is unfortunately nphard if one wishes to guarantee an optimal anonymization 8. We give a summary on the stateoftheart health care data anonymization issues including legal. Citeseerx data privacy through optimal kanonymization. To achieve optimal and practical k anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. Achieving kanonymity privacy protection using generalization and suppression.
An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In international conference on data engineering, pages 217228, 2005. Classification and analysis of anonymization techniques. Ldiversity and tcloseness models are refinements of kanonymity. Survey on privacy preserving data mining techniques in health. When releasing microdata for research purposes, one needs to preserve the privacy of respondents while maximizing data utility. Jul 11, 2017 publishing raw electronic health records ehrs may be considered as a breach of the privacy of individuals because they usually contain sensitive information. The concept of k anonymity was first introduced by latanya sweeney and pierangela samarati in a paper published in 1998 as an attempt to solve the problem.
Through experiments on real census data, we show the resulting algorithm can find optimal kanonymizations under two representative cost measures and a wide range of k. The technique of k anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. Since the k anonymization problem is an nphard, we show that our algorithm can efficiently find an optimal k anonymity solutions with. Since the kanonymization problem is an nphard, we show that our algorithm can efficiently find an optimal kanonymity solutions with exploiting such special characteristics of the igh data, i. A truthful data anonymization algorithm with strong. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Survey on privacy preserving data mining techniques in. Utilitypreserving anonymization for health data publishing. It is the responsibility of the data holder to ensure privacy of the users data.
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