Privacy Preservation for Healthcare System Using T-Closeness Through Microaggregation (original) (raw)
Related papers
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...
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.
t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation
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.
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
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggregation
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
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.
G-Model: A Novel Approach to Privacy-Preserving 1:M Microdata Publication
2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), 2020
Public availability of electronic health records raises major privacy concerns, as that data contains confidential personal information of individuals. Publishing such data must be accompanied by appropriate privacy-preserving techniques to avoid or at least minimize privacy breaches. The task of privacy preservation becomes even more challenging when the data have multiple sensitive attributes (SAs). Privacy risks increase even further when an individual has multiple records (1:M) in a dataset, a rather typical situation with electronic health records (EHRs). To overcome these privacy issues, the methodologies known as 1:M generalization and l-anatomy have been proposed by the research community. However, these models fail to provide optimal privacy protection, data utility and security against certain types of attacks, such as gender-specific SA attacks. In this paper, we propose a generic 1:M data privacy model, called G-model, which provides guaranteed data privacy with high data utility and no information loss. Our G-model maintains separate groups and caches of male and female SAs, thus protecting privacy against gender-specific SA attacks. Furthermore, G-model avoids generalization, thus providing high data utility with no information loss. Experiments performed on three real-world datasets (Adult, Informs, and YouTube datasets) have shown that the proposed model is more efficient and better at privacy protection than the existing models from the literature.
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.
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.
K−Means Clustering Microaggregation for Statistical Disclosure Control
Advances in Intelligent Systems and Computing, 2012
This paper presents a K-means clustering technique that satisfies the biobjective function to minimize the information loss and maintain k-anonymity. The proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation methods for all of the test situations.