t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation (original) (raw)
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ABSTRACT: The preservation of privacy of distributed micro data is basic to keep the sensitive data of people from being disclosed. Many privacy models are used for ensuring the privacy of micro data. Micro aggregation is a strategy for disclosure restraint went for securing the security of information subjects in micro data discharges .It has been utilized as another option to generalization as well as suppression to create kunidentified datasets, where the character of every subject is covered up inside a gathering of k subjects. Not like the generalization, micro aggregation annoys the information and this extra concealing flexibility permits enhancing information usefulness in few courses, such as, rising information granularity, decreasing the consequence of outliers, and maintaining a strategic distance from the discretization of information. Kanonymity, taking place the opposite side, doesn’t secure against field exposure, which happens if the changeability of secure fields in a gathering of k subjects is too little. In this paper, the conservation of privacy of micro data discharged in health care service systems is engaged through micro aggregation by utilizing tcloseness which is a more adaptable privacy model guaranteeing strictest security. Previous algorithms used to create t-close datasets depend on generalization and suppression. This paper proposes, how micro aggregation useful in healthcare service systems to produce t-close datasets using k-anonymous data. Micro aggregation algorithm is presented for t-close datasets using k-anonymous data, and the purposes of micro aggregation are analyzed.
Privacy Preservation for Healthcare System Using T-Closeness Through Microaggregation
2017
The preservation of privacy of published microdata is essential to prevent the sensitive information of individuals from being disclosed. Several privacy models are used for protecting the privacy of microdata. Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers, and avoiding discretization of numerical data. k-Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of k subjects is too small . In this paper, the preservation of privac...
cegon technologies, 2019
Microaggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in microdata releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, microaggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity, reducing the impact of outliers and avoiding discretization of numerical data. k-Anonymity, on the other side, does not protect against attribute disclosure, which occurs if the variability of the confidential values in a group of k subjects is too small. To address this issue, several refinements of k-anonymity have been proposed, among which t-closeness stands out as providing one of the strictest privacy guarantees. Existing algorithms to generate t-close data sets are based on generalization and suppression (they are extensions of k-anonymization algorithms based on the same principles). This paper proposes and shows how to use microaggregation to generate k-anonymous t-close data sets. The advantages of microaggregation are analyzed, and then several microaggregation algorithms for k-anonymous t-closeness are presented and empirically evaluated. EXISTING SYSTEM: Same as for k-anonymity, the most common way to attain t-closeness is to use generalization and suppression. In fact, the algorithms for k-anonymity based on those principles can be adapted to yield t-closeness by adding the t-closeness constraint in the search for a feasible
New Multi-dimensional Sorting Based K-Anonymity Microaggregation for Statistical Disclosure Control
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2013
In recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a new microaggregation technique for Statistical Disclosure Control (SDC). It consists of two stages. In the first stage, the algorithm sorts all the records in the data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage an optimal microaggregation method is used to create k-anonymous clusters while minimizing the information loss. It works by taking the sorted data and simultaneously creating two distant clusters using the two extreme sorted values as seeds for the clusters. The performance of the proposed technique is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithm has the lowest information loss compared with a basket of techniques in the literature.
Improving the Utility of Differentially Private Data Releases via k-Anonymity
2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2013
A common view in some data anonymization literature is to oppose the "old" k-anonymity model to the "new" differential privacy model, which offers more robust privacy guarantees. However, the utility of the masked results provided by differential privacy is usually limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with the general-purpose anonymized data resulting from k-anonymity mechanisms, which also focus on preserving data utility. In this paper, we show that a synergy between differential privacy and k-anonymity can be found when the objective is to release anonymized data: k-anonymity can help improving the utility of the differentially private release. Specifically, we show that the amount of noise required to fulfill ε-differential privacy can be reduced if noise is added to a k-anonymous version of the data set, where k-anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the analytical utility of the anonymized output data set is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on a reference data set.
Extended K-Anonymity Model for Privacy Preserving on Micro Data
International Journal of Computer Network and Information Security, 2015
Today, information collectors, particularly statistical organizations, are faced with two conflicting issues. On one hand, according to their natural responsibilities and the increasing demand for the collected data, they are committed to propagate the information more extensively and with higher quality and on the other hand, due to the public concern about the privacy of personal information and the legal responsibility of these organizations in protecting the private information of their users, they should guarantee that while providing all the information to the population, the privacy is reasonably preserved. This issue becomes more crucial when the datasets published by data mining methods are at risk of attribute and identity disclosure attacks. In order to overcome this problem, several approaches, called p-sensitive k-anonymity, p+-sensitive k-anonymity, and (p, α)-sensitive k-anonymity, were proposed. The drawbacks of these methods include the inability to protect micro datasets against attribute disclosure and the high value of the distortion ratio. In order to eliminate these drawbacks, this paper proposes an algorithm that fully protects the propagated micro data against identity and attribute disclosure and significantly reduces the distortion ratio during the anonymity process.
2017
1 Sonu Khapekar, PG Scholar, Department of CSE, NMIET, Pune, Maharashtra. 2 Prof. Lomesh Ahire, Department of CSE, NMIET, Pune, Maharashtra. ---------------------------------------------------------------------***---------------------------------------------------------------------------AbstractThe preservation of privacy of published micro data is essential to prevent the sensitive information of individuals from being disclosed. Several privacy models are used for protecting the privacy of micro data. Micro aggregation is a technique for disclosure limitation aimed at protecting the privacy of data subjects in micro data releases. It has been used as an alternative to generalization and suppression to generate k-anonymous data sets, where the identity of each subject is hidden within a group of k subjects. Unlike generalization, micro aggregation perturbs the data and this additional masking freedom allows improving data utility in several ways, such as increasing data granularity...
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
2007 IEEE 23rd International Conference on Data Engineering, 2007
The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of-diversity has been proposed to address this;diversity requires that each equivalence class has at least well-represented values for each sensitive attribute. In this paper we show that-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We choose to use the Earth Mover Distance measure for our t-closeness requirement. We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments.
A pairwise-systematic microaggregation for statistical disclosure control
Proceedings of the 10th IEEE …, 2011
Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Microaggregation for SDC is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least records and then replacing the records in each group with the centroid of the group. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a pairwise systematic (P-S) microaggregation method to minimize the information loss. The proposed technique simultaneously forms two distant groups at a time with the corresponding similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of P-S problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the P-S algorithm is compared against the most recent microaggregation methods. Experimental results show that P-S algorithm incurs less than half information loss than the latest microaggregation methods for all of the test situations.
Publishing microdata with a robust privacy guarantee
Proceedings of the VLDB Endowment, 2012
Today, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this condition. Yet, no method proposed to date explicitly bounds the percentage of information an adversary gains after seeing the published data for each sensitive value therein. This paper introduces β-likeness, an appropriately robust privacy model for microdata anonymization, along with two anonymization schemes designed therefor, the one based on generalization, and the other based on perturbation. Our model postulates that an adversary's confidence on the likelihood of a certain sensitive-attribute (SA) value should not increase, in relative difference terms, by more than a predefined threshold. Our techniques aim to satisfy a given β threshold with little information loss. We experimentally demonstrate that (i) our model provides an effective privacy guarantee in a way that predecessor models cannot, (ii) our generalization scheme is more effective and efficient in its task than methods adapting algorithms for the k-anonymity model, and (iii) our perturbation method outperforms a baseline approach. Moreover, we discuss in detail the resistance of our model and methods to attacks proposed in previous research.