K-anonymity a model for protecting privacy bibtex books

Citeseerx protecting privacy when disclosing information. The kanonymization technique has been developed to deassociate. To address the privacy issue, many approaches 1, 2 have been proposed in the literature over the past few years. However, information loss and data utility are the prime issues in the anonymization based approaches as discussed in 415, 17. However, our empirical results show that the baseline k anonymity model is very conservative in terms of reidentification risk under the journalist reidentification scenario. International journal of uncertainty,fuzziness and knowledgebased systems, 2002, 105. The baseline kanonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k anonymity for a wide range of mobile clients with contextsensitive privacy requirements. A new heuristic anonymization technique for privacy. 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.

The models explained are 1 private information retrieval, 2 ir with homomorphic encryption, 3 kanonymity, 4 ldiversity, and finally 5 defamation caused by kanonymity published in. A new heuristic anonymization technique for privacy preserved. In this paper, we study how to use k anonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by qi attributes and sensitive attribute itself, use bkl, k clustering to present equivalent class with diversity. The concept of personalized privacy in 19 allows data owners to choose the level of generalization of sensitive attribute and to integrate it with kanonymity to produce a stronger anonymized version of the data. The representative heuristic algorithm datafly5 implements kanonymity by fulldomain generalization. To address this limitation of kanonymity, machanavajjhala et al. Most of them are based on location perturbation and obfuscation, which employ wellknown privacy metrics such as kanonymity 3 and rely on a trusted thirdparty server. To surmount those shortcomings, i propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing on cloud. In this paper, we study how to use kanonymity in uncertain data set, use influence matrix of background knowledge to describe the influence degree of sensitive attribute produced by qi attributes and sensitive attribute itself, use bkl,kclustering to present. From kanonymity to diversity the protection kanonymity provides is simple and easy to understand. The solution provided in this paper includes a formal protection model named kanonymity and a set of accompanying policies for deployment. The concept of kanonymity was originally introduced in.

Let rta 1, a n be a table and qi rt be the quasiidentifier associated with it. At times there is a need however for management or statistical purposes based on personal information in aggregated form. International journal on uncertainty, fuzziness and knowledgebased systems,10 5, 2002. The k anonymization technique has been developed to deassociate sensitive attributes and anonymise. Part of the lecture notes in computer science book series lncs, volume 3654. Methods for kanonymity can be divided into two groups. The realworld algorithms datafly and argus are compared to mingen. Achieving kanonymity in privacyaware locationbased.

The new introduced privacy model avoids this shortcoming. The simulation results show that the proposed algorithm is superior to the individual search algorithm in average. Protect peoples privacy, when releasing personspecific information limit the ability of using the quasiidentifier to link other external information kanonymity table change data in such a way that for each tuple in the resulting table there are at least k1 other tuples with the same value for. 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. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location kanonymity for a wide range of mobile clients with contextsensitive privacy requirements. The use of recommendation systems has grown widely in recent years, helping people choose which movies to watch, books to read, and items. In this paper, we focus on a study on the kanonymity property 11, 10. Forthcoming book entitled, the identifiability of data. The concept of kanonymity was first introduced by latanya sweeney and pierangela samarati in a paper published in 1998 as an attempt to solve the problem. Preserve the privacy of anonymous and confidential. Research on privacy protection based on kanonymity ieee xplore.

The preferred minimal generalization algorithm mingen, which is a theoretical algorithm presented herein, combines these techniques to provide k anonymity protection with minimal distortion. For this purpose, two algorithms, tabu search and genetic algorithm, are combined. Experimental results revealed the superiority and outperformance of the developed technique than kanonymity, ldiversity, and. In traditional database domain, k anonymity is a hotspot in data publishing for privacy protection. K anonymity is an important model that prevents joining attacks in privacy protecting. Privacypreserving distributed kanonymity springerlink. So there is requirement of certain data to be published and exchanging of the.

Protecting privacy using kanonymity with a hybrid search. Pierangela samarati security, privacy, data protection, kanonymity. Protecting privacy using kanonymity journal of the. The model requires that the microdata is partitioned into a set of equivalence classes, each. This paper provides a formal presentation of combining generalization and suppression to achieve kanonymity.

Situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. Page 2 so a common practice is for organizations to release and receive personspecific data with all explicit identifiers, such as name, address and telephone. Two necessary conditions to achieve psensitive k anonymity property are presented, and used in developing algorithms to create masked microdata with psensitive k anonymity property using generalization and suppression. Index termskanonymity, location privacy, locationbased applications, mobile computing systems. Two necessary conditions to achieve psensitive kanonymity property are presented, and used in developing algorithms to create masked microdata with psensitive kanonymity property using generalization and suppression. T is said to satisfy kanonymity with respect to qi i. However, our empirical results show that the baseline kanonymity model is very conservative in terms of reidentification risk under the journalist reidentification scenario. The kanonymity model has been extensively studied recently because of its relative conceptual simplicity and effectiveness e. Privacy protectin models and defamation caused by kanonymity. The proper protection of personal information is increasingly becoming an important issue in an age where misuse of personal information and identity theft are widespread. In this paper, we introduce a new privacy protection property called psensitive kanonymity. To protect the privacy of the individual, sweeney et al. Carnegie mellon university, laboratory for international data privacy. Research on kanonymity algorithm in privacy protection.

Among the various anonymization approaches, the kanonymity model has been significantly used in privacy preserving data mining because of its simplicity and efficiency. Todays globally networked society places great demand on the dissemination and sharing of personspecific data. A kanonymity based semantic model for protecting personal. A release provides k anonymity protection if the information for each person contained in the release cannot be distinguished from at least k1 individuals whose information also appears in the release. An extensive study on data anonymization algorithms based on. The solution provided in this paper includes a formal protection model named k anonymity and a set of accompanying policies for deployment. Part of the lecture notes in computer science book series lncs, volume 4176. International journal on uncertainty, fuzziness and knowledgebased systems, 10. In this paper,we proposetwo newprivacyprotectionmodels called p.

In other words, kanonymity requires that each equivalence class contains at least k records. Examples include locationaware emergency response, locationbased advertisement, and locationbased entertainment. Many researchers do research on kanonymity and have proposed various ways to implement kanonymity. View notes kanonymity a model for protecting privacy from cs 254 at wave lake havasu high school.

This article based on the existing kanonymity privacy preservation of the basic ideas and concepts, kanonymity model, and enhanced the kanonymity model, and gives a simple example to compare each algorithm. Rt is said to satisfy kanonymity if and only if each sequence of values in rtqi rt appears with at least k occurrences in rtqi rt. Uncertain data privacy protection based on kanonymity via. Minimum cost kanonymity obviously, we can guarantee kanonymity by replacing every cell with a, but this renders the database useless. Novel approaches for privacy preserving data mining in k. An extensive study on data anonymization algorithms based. This paper will analyse comprehensively the current research situation of k anonymity model used to prevent privacy leaked in data publishing, introduce the. Study on privacy protection algorithm based on kanonymity. The cost of kanonymous solution to a database is the number of s introduced.

The kanonymity protection model is important because it forms the basis on which the realworld systems known as datafly, margus and ksimilar provide guarantees of privacy protection. Create marketing content that resonates with prezi video. Many works have been conducted to achieve kanonymity. The k anonymity protection model is important because it forms the basis on which the realworld systems known as datafly, margus and ksimilar provide guarantees of privacy protection. Achieving kanonymity privacy protection using generalization. Many researchers do research on k anonymity and have proposed various ways to implement k anonymity. View notes k anonymity a model for protecting privacy from cs 254 at wave lake havasu high school. Given personspecific fieldstructured data, produce a release of the data with scientific guarantees that the individuals who are the subjects of the data cannot.

In field of it sector to maintain privacy and confidentiality of data is very important for decision making. The blue social bookmark and publication sharing system. A release provides k anonymity protection if the information for each person contained in the release cannot be distinguished from at least k 1 individuals whose information also appears in the release. Introduction the privacy of individuals is a challenging task in a. Kanonymity is an important model that prevents joining attacks in privacy protecting. To achieve kanonymity, a lbs related query is submitted. Jan 09, 2008 the baseline k anonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. The baseline k anonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. The concept of personalized privacy in 19 allows data owners to choose the level of generalization of sensitive attribute and to integrate it with k anonymity to produce a stronger anonymized version of the data. As a result, there has been a lot of research on how to transform a dataset into a \k\ anonymous table. While algorithms exist for producing kanonymous data, the model. Efficient data anonymization model selector for privacypreserving data.

Data and applications security xix pp 166177 cite as. Protecting location privacy with personalized kanonymity. The kanonymity protection model is important because it forms the basis on which. In traditional database domain, kanonymity is a hotspot in data publishing for privacy protection. The solution provided in this paper includes a formal protection model named k anonymity and a set. Different from previous the psensitive kanonymity model, these new introduced models allow us to release a lot more information without compromising privacy. An important challenge in the wide deployment of locationbased services lbss is the privacy aware management of location information, providing. Many works have been conducted to achieve k anonymity. International journal of uncertainty, fuzziness and knowledgebased systems 105, 557570. Their approaches towards disclosure limitation are quite di erent. Continued advances in mobile networks and positioning technologies have created a strong market push for locationbased applications. Different releases of the same private table can be linked together to compromise kanonymity.

A model for protecting privacy consider a data holder, such as a hospital or a bank, that has a. An important challenge in the wide deployment of locationbased services lbss is the privacyaware. Methods for k anonymity can be divided into two groups. A minimum cost kanonymity solution suppresses the fewest number of cells necessary to guarantee kanonymity. While kanonymity protects against identity disclosure, it is insuf. International journal on uncertainty, fuzziness and knowledgebased systems 105 2002 p557570. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Index terms kanonymity, database, privacy protection, heuristic algorithm.