It allows a wide range of users to benefit from the location privacy protection with personalized privacy requirements. 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. The preferred minimal generalization algorithm mingen, which is a theoretical algorithm presented herein, combines these techniques to provide k anonymity protection with minimal distortion. A model for protecting privacy consider a data holder, such as a hospital or a bank, that has a privately held collection of personspecific, field structured data. Achieving kanonymity in privacyaware locationbased services. The realworld algorithms datafly and argus are compared to mingen. The representative heuristic algorithm datafly5 implements kanonymity by fulldomain generalization. Kanonymity is an important model that prevents joining attacks in privacy protecting. In traditional database domain, k anonymity is a hotspot in data publishing for privacy protection. Study on privacy protection algorithm based on kanonymity. Research of differential privacy protection on social. An important challenge in the wide deployment of locationbased services lbss is the privacyaware management of location information, providing.
The kanonymization technique has been developed to deassociate sensitive attributes and anonymise. Research on kanonymity algorithm in privacy protection. A customizable k anonymity model for protecting privacy of location data has two unique features. In this paper,we proposetwo newprivacyprotectionmodels called p. In this paper, we focus on a study on the kanonymity property 11, 10. Achieving kanonymity privacy protection using generalization. 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.
A hypothetical example of the three databases assumed in the kanonymity privacy model under the journalist. It provides a customizable framework to support k anonymity with variable k. Di erential privacy is another popular privacy model that is often opposed to kanonymity like models. For instance, with respect to the microdata table in fig. International journal on uncertainty, fuzziness and knowledgebased systems,10 5, 2002. International journal on uncertainty, fuzziness and knowledgebased systems, 10. A model for protecting privacy find, read and cite all the research you. 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. At times there is a need however for management or statistical purposes based on personal information in aggregated form.
This paper presents a k anonymity protocol when the data is vertically partitioned between. Preserve the privacy of anonymous and confidential. A customizable kanonymity model for protecting privacy of location data has two unique features. While algorithms exist for producing kanonymous data, the model has been that of a single source wanting to publish data. Many works have been conducted to achieve k anonymity. The baseline kanonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. The kanonymity protection model is important because it forms the basis on which the realworld systems known as datafly, argus and ksimilar provide. In this paper, we propose a new model, called k presencesecrecy, that prevents such adversaries from inferring whether an arbitrary individual is included in a personal data table. 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. Download bibtex over the last twenty years, there has been a tremendous growth in the amount of private data collected about individuals. Examples include locationaware emergency response, locationbased advertisement, and locationbased entertainment. 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. Most of them are based on location perturbation and obfuscation, which employ wellknown privacy metrics such as k anonymity 3 and rely on a trusted thirdparty server. Protecting privacy using kanonymity journal of the.
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. However, kanonymity does not guarantee privacy against adversaries who have knowledge of even a few uncommon individuals in a population. Sweeney l 2002 achieving kanonymity privacy protection using generalization and suppression international journal of uncertainty, fuzziness and knowledgebased systems 10 571588. Create marketing content that resonates with prezi video. One of the big challenges in wide deployment of lbs systems. 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. Many researchers do research on k anonymity and have proposed various ways to implement k anonymity. The representative heuristic algorithm datafly5 implements k anonymity by fulldomain generalization. In other words, kanonymity requires that each equivalence class contains at least k records. The blue social bookmark and publication sharing system. Many researchers do research on kanonymity and have proposed various ways to implement kanonymity. Among the various anonymization approaches, the kanonymity model has been significantly used in privacy preserving data mining because of its simplicity and efficiency. Page 2 so a common practice is for organizations to release and receive personspecific data with all explicit.
It provides a customizable framework to support kanonymity with variable k. Introduction the privacy of individuals is a challenging task in a. Methods for k anonymity can be divided into two groups. Novel approaches for privacy preserving data mining in k. Protecting privacy using kanonymity journal of the american. However, k anonymity does not guarantee privacy against adversaries who have knowledge of even a few uncommon individuals in a population. International journal on uncertainty, fuzziness and knowledgebased systems 105 2002 p557. A release provides kanonymity 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. To address this limitation of kanonymity, machanavajjhala et al. Let rta 1, a n be a table and qi rt be the quasiidentifier associated with it.
An extensive study on data anonymization algorithms based. Methods for kanonymity can be divided into two groups. The simulation results show that the proposed algorithm is superior to the individual search algorithm in average. However, our empirical results show that the baseline kanonymity model is very conservative in terms of reidentification risk under the journalist reidentification scenario. The baseline k anonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. Index termskanonymity, location privacy, locationbased applications, mobile computing systems. Jan 09, 2008 the baseline k anonymity 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 kanonymity for a wide range of mobile clients with contextsensitive privacy requirements.
A kanonymity based semantic model for protecting personal. 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. International journal on uncertainty, fuzziness and knowledgebased systems, 10 5, 2002. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Achieving kanonymity privacy protection using generalization and. International journal of uncertainty, fuzziness and knowledgebased systems, 10 7, 2002. To achieve kanonymity, a lbs related query is submitted. Protecting privacy using kanonymity with a hybrid search. International journal on uncertainty, fuzziness and knowledgebased systems 105 2002 p557570. Privacy protectin models and defamation caused by kanonymity. One of the big challenges in wide deployment of lbs. International journal of uncertainty,fuzziness and knowledgebased systems, 2002, 105. A customizable kanonymity model for protecting location.
Different from previous the psensitive kanonymity model, these new introduced models allow us to release a lot more information without compromising privacy. The k anonymization technique has been developed to deassociate sensitive attributes and anonymise. The solution provided in this paper includes a formal protection model named k anonymity and a set of accompanying policies for deployment. An enhanced k anonymity model for privacy preserving data publishing proc. This paper provides a formal presentation of combining generalization and suppression to achieve kanonymity. This paper presents a kanonymity protocol when the data is vertically partitioned between. 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. Protecting privacy using kanonymity with a hybrid search scheme. In this paper, we propose a new model, called kpresencesecrecy, that prevents such adversaries from inferring whether an arbitrary individual is included in a personal data table. However, our empirical results show that the baseline k anonymity model is very conservative in terms of reidentification risk under the journalist reidentification scenario. 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. 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. While kanonymity protects against identity disclosure, it is insuf.
Index terms kanonymity, database, privacy protection, heuristic algorithm. K anonymity is an important model that prevents joining attacks in privacy protecting. Many works have been conducted to achieve kanonymity. Examples include locationaware emergency services, location based service advertisement, and location sensitive billing. If insertion of record satisfies the kanonymity then such record is inserted in table and suppressed the sensitive information attribute by to maintain the kanonymity in database. The solution provided in this paper includes a formal protection model named kanonymity and a set of accompanying policies for deployment.
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. With the rapid growth in database, networking, and computing technologies, such data can be integrated and analyzed digitally. Continued advances in mobile networks and positioning technologies have created a strong market push for locationbased services lbss. Our proposal can be combined with kanonymity re nements such as ldiversity and tcloseness, hence yielding simultaneous utility and privacy guarantees. To address the privacy issue, many approaches 1, 2 have been proposed in the literature over the past few years. 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 equivalent class with diversity. A model for protecting privacy 1 latanya sweeney school of computer science, carnegie mellon university, pittsburgh, pennsylvania, usa email. Ola optimal lattice anonymization is an efficient fulldomain optimal algorithm among these works. Generalization involves replacing or recoding a value with a less specific but semantically consistent value.
Protecting location privacy with personalized kanonymity. However, our empirical results show that the baseline kanonymity model is very conservative in terms of reidentification. 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. A model for protecting privacy consider a data holder, such as a hospital or a bank, that has a. While algorithms exist for producing k anonymous data, the model has been that of a single source wanting to publish data. 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. Uncertain data privacy protection based on kanonymity via.
In traditional database domain, kanonymity is a hotspot in data publishing for privacy protection. For this purpose, two algorithms, tabu search and genetic algorithm, are combined. The cost of kanonymous solution to a database is the number of s introduced. 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. However, information loss and data utility are the prime issues in the anonymization based approaches as discussed in 415, 17. Continued advances in mobile networks and positioning technologies have created a strong market push for locationbased applications. The kanonymity model has been extensively studied recently because of its relative conceptual simplicity and effectiveness e. A minimum cost kanonymity solution suppresses the fewest number of cells necessary to guarantee kanonymity. Minimum cost kanonymity obviously, we can guarantee kanonymity by replacing every cell with a, but this renders the database useless. To achieve k anonymity, a lbs related query is submitted. T is said to satisfy kanonymity with respect to qi i. An important challenge in the wide deployment of locationbased services lbss is the privacy aware management of location information, providing. 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.
1213 137 778 1241 1214 418 1421 1327 136 1034 1370 226 786 282 864 1218 624 349 136 1215 1503 497 341 1054 659 175 1130 15 430 116 288 860 1233 30 206 1261 1247