Achieving k-anonymity using full domain generalization

Preserving privacy while publishing data has emerged as key research area in data security and has become a primary issue in publishing person specific sensitive information. How to preserve one's privacy efficiently is a critical issue while publishing data. K-anonymity is a key technique for de-identifying the sensitive datasets. In our work, we have described a framework to implement most of the k-anonymity algorithms and also proposed a novel scheme that produces better results with real-world datasets. Additionally, we suggest a new approach that attains better results by applying a novel approach and exploiting various characteristic of our suggested framework. The proposed approach uses the concept of breadth- search algorithm to generalize the lattice in bottom-up manner. the proposed algorithm generates the paths using predictive tagging of the nodes in the lattice in vertically.the proposed algorithm has less execution time than other full domain generalization algorithms for k-anonymization

Utility-Based Privacy Preserving Data Publishing

Advances in data collection techniques and need for automation triggered in proliferation of a huge amount of data. This exponential increase in the collection of personal information has for some time represented a serious threat to privacy. With the advancement of technologies for data storage, data mining, machine learning, social networking and cloud computing, the problem is further fueled. Privacy is a fundamental right of every human being and needs to be preserved. As a counterbalance to the socio-technical transformations, most nations have both general policies on preserving privacy and specic legislation to control access to and use of data. Privacy preserving data publishing is the ability to control the dissemination and use of one's personal information. Mere publishing (or sharing) of original data in raw form results in identity disclosure with linkage attacks. To overcome linkage attacks, the techniques of statistical disclosure control are employed. One such approach is k-anonymity that reduce data across a set of key variables to a set of classes. In a k-anonymized dataset each record is indistinguishable from at least k-1 others, meaning that an attacker cannot link the data records to population units with certainty thus reducing the probability of disclosure. Algorithms that have been proposed to enforce k-anonymity are Samarati's algorithm and Sweeney's Datafly algorithm. Both of these algorithms adhere to full domain generalization with global recording. These methods have a tradeo between utility, computing time and information loss. A good privacy preserving technique should ensure a balance of utility and privacy, giving good performance and level of uncertainty. In this thesis, we propose an improved greedy heuristic that maintains a balance between utility, privacy, computing time and information loss. Given a dataset and k, constructing the dataset to k-anonymous dataset can be done by the above-mentioned schemes. One of the challenges is to nd the best value of k, when the dataset is provided. In this thesis, a scheme has been proposed to achieve the best value of k for a given dataset. The k-anonymity scheme suers from homogeneity attack. As a result, the l-diverse scheme was developed. It states that the diversity of domain values of the dataset in an equivalence class should be l. The l-diversity scheme suers from background knowledge attack. To address this problem, t-closeness scheme was proposed. The t-closeness principle states that the distribution of records in an equivalence class and the distribution of records in the table should not exceed more than t. The drawback with this scheme is that, the distance metric deployed in constructing a table, satisfying t-closeness, does not follow the distance characteristics. In this thesis, we have deployed an alternative distance metric namely, Hellinger metric, for constructing a t-closeness table. The t-closeness scheme with this alternative distance metric performed better with respect to the discernability metric and computing time. The k-anonymity, l-diversity and t-closeness schemes can be used to anonymize the dataset before publishing (releasing or sharing). This is generally in a static environment. There are also data that need to be published in a dynamic environment. One such example is a social network. Anonymizing social networks poses great challenges. Solutions suggested till date do not consider utility of the data while anonymizing. In this thesis, we propose a novel scheme to anonymize the users depending on their importance and take utility into consideration. Importance of a node was decided by the centrality and prestige measures. Hence, the utility and privacy of the users are balanced

Algorithms to anonymize structured medical and healthcare data:A systematic review

Introduction: With many anonymization algorithms developed for structured medical health data (SMHD) in the last decade, our systematic review provides a comprehensive bird’s eye view of algorithms for SMHD anonymization. Methods: This systematic review was conducted according to the recommendations in the Cochrane Handbook for Reviews of Interventions and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Eligible articles from the PubMed, ACM digital library, Medline, IEEE, Embase, Web of Science Collection, Scopus, ProQuest Dissertation, and Theses Global databases were identified through systematic searches. The following parameters were extracted from the eligible studies: author, year of publication, sample size, and relevant algorithms and/or software applied to anonymize SMHD, along with the summary of outcomes. Results: Among 1,804 initial hits, the present study considered 63 records including research articles, reviews, and books. Seventy five evaluated the anonymization of demographic data, 18 assessed diagnosis codes, and 3 assessed genomic data. One of the most common approaches was k-anonymity, which was utilized mainly for demographic data, often in combination with another algorithm; e.g., l-diversity. No approaches have yet been developed for protection against membership disclosure attacks on diagnosis codes. Conclusion: This study reviewed and categorized different anonymization approaches for MHD according to the anonymized data types (demographics, diagnosis codes, and genomic data). Further research is needed to develop more efficient algorithms for the anonymization of diagnosis codes and genomic data. The risk of reidentification can be minimized with adequate application of the addressed anonymization approaches. Systematic Review Registration: [http://www.crd.york.ac.uk/prospero], identifier [CRD42021228200].</p

LACE: Supporting Privacy-Preserving Data Sharing in Transfer Defect Learning

Cross Project Defect Prediction (CPDP) is a field of study where an organization lacking enough local data can use data from other organizations or projects for building defect predictors. Research in CPDP has shown challenges in using ``other\u27\u27 data, therefore transfer defect learning has emerged to improve on the quality of CPDP results. With this new found success in CPDP, it is now increasingly important to focus on the privacy concerns of data owners.;To support CPDP, data must be shared. There are many privacy threats that inhibit data sharing. We focus on sensitive attribute disclosure threats or attacks, where an attacker seeks to associate a record(s) in a data set to its sensitive information. Solutions to this sharing problem comes from the field of Privacy Preserving Data Publishing (PPDP) which has emerged as a means to confuse the efforts of sensitive attribute disclosure attacks and therefore reduce privacy concerns. PPDP covers methods and tools used to disguise raw data for publishing. However, prior work warned that increasing data privacy decreases the efficacy of data mining on privatized data.;The goal of this research is to help encourage organizations and individuals to share their data publicly and/or with each other for research purposes and/or improving the quality of their software product through defect prediction. The contributions of this work allow three benefits for data owners willing to share privatized data: 1) that they are fully aware of the sensitive attribute disclosure risks involved so they can make an informed decision about what to share, 2) they are provided with the ability to privatize their data and have it remain useful, and 3) the ability to work with others to share their data based on what they learn from each others data. We call this private multiparty data sharing.;To achieve these benefits, this dissertation presents LACE (Large-scale Assurance of Confidentiality Environment). LACE incorporates a privacy metric called IPR (Increased Privacy Ratio) which calculates the risk of sensitive attribute disclosure of data through comparing results of queries (attacks) on the original data and a privatized version of that data. LACE also includes a privacy algorithm which uses intelligent instance selection to prune the data to as low as 10% of the original data (thus offering complete privacy to the other 90%). It then mutates the remaining data making it possible that over 70% of sensitive attribute disclosure attacks are unsuccessful. Finally, LACE can facilitate private multiparty data sharing via a unique leader-follower algorithm (developed for this dissertation). The algorithm allows data owners to serially build a privatized data set, by allowing them to only contribute data that are not already in the private cache. In this scenario, each data owner shares even less of their data, some as low as 2%.;The experiments of this thesis, lead to the following conclusion: at least for the defect data studied here, data can be minimized, privatized and shared without a significant degradation in utility. Specifically, in comparative studies with standard privacy models (k-anonymity and data swapping), applied to 10 open-source data sets and 3 proprietary data sets, LACE produces privatized data sets that are significantly smaller than the original data (as low as 2%). As a result LACE offers better protection against sensitive attribute disclosure attacks than other methods

Anonimização de Dados em Educação

Interest in data privacy is not only growing, but the quantity of data collected is also increasing. This data, which is collected and stored electronically, contains information related with all aspects of our lives, frequently containing sensitive information, such as financial records, activity in social networks, location traces collected by our mobile phones and even medical records. Consequently, it becomes paramount to assure the best protection for this data, so that no harm is done to individuals even if the data is to become publicly available. To achieve it, it is necessary to avoid the linkage between records in a dataset and a real world individual. Despite some attributes, such as gender and age, though alone they can not identify a corresponding individual, their combination with other datasets can lead to the existence of unique records in the dataset and a consequent linkage to a real world individual. Therefore, with data anonymization, it is possible to assure, with various degrees of protection, that said linkage is avoided the best we can. However, this process can have a decline in data utility as consequence. In this work, we explore the terminology and some of the techniques that can be used during the process of data anonymization. Moreover, we show the effects of said techniques on information loss, data utility and re-identification risk, when applied to a dataset with personal information collected from college graduated students. Finally, and once the results are presented, we perform an analysis and comparative discussion of the obtained results.Hoje em dia é possível observar que tanto a preocupação com a privacidade dos dados pessoais como a quantidade de dados recolhidos estão a aumentar. Estes dados, recolhidos e armazenados eletronicamente, contêm informação relacionada com todos os aspetos das nossas vidas, informação essa muitas vezes sensível, tal como registos financeiros, atividade em redes sociais, rastreamento de dispositivos móveis e até registos médicos. Consequentemente, torna-se vital assegurar a proteção destes dados para que, mesmo se tornados públicos, não causem danos pessoais aos indivíduos envolvidos. Para isso, é necessário evitar que registos nos dados sejam associados a indivíduos reais. Apesar de atributos, como o género e a idade, singularmente não conseguirem identificar o individuo correspondente, a sua combinação com outros conjuntos de dados, pode levar à existência de um registo único no conjunto de dados e consequente associação a um individuo. Com a anonimização dos dados, é possível assegurar, com variados graus de proteção, que essa associação a um individuo real seja evitada ao máximo. Contudo, este processo pode ter como consequência uma diminuição na utilidade dos dados. Com este trabalho, exploramos a terminologia e algumas das técnicas que podem ser utilizadas no processo de anonimização de dados. Mostramos também os efeitos dessas várias técnicas tanto na perda de informação e utilidade dos dados, como no risco de re-identificação associado, quando aplicadas a um conjunto de dados com informação pessoal recolhida a alunos que conluíram o ensino superior. No final, e uma vez feita a apresentação dos resultados, é feita uma análise e discussão comparativa dos resultados obtidos

Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks

Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information to multiple sinks and reduce energy consumption

Does k-anonymous microaggregation affect machine-learned macrotrends?

n the era of big data, the availability of massive amounts of information makes privacy protection more necessary than ever. Among a variety of anonymization mechanisms, microaggregation is a common approach to satisfy the popular requirement of k-anonymity in statistical databases. In essence, k-anonymous microaggregation aggregates quasi-identifiers to hide the identity of each data subject within a group of other k - 1 subjects. As any perturbative mechanism, however, anonymization comes at the cost of some information loss that may hinder the ulterior purpose of the released data, which very often is building machine-learning models for macrotrends analysis. To assess the impact of microaggregation on the utility of the anonymized data, it is necessary to evaluate the resulting accuracy of said models. In this paper, we address the problem of measuring the effect of k-anonymous microaggregation on the empirical utility of microdata. We quantify utility accordingly as the accuracy of classification models learned from microaggregated data, and evaluated over original test data. Our experiments indicate, with some consistency, that the impact of the de facto microaggregation standard (maximum distance to average vector) on the performance of machine-learning algorithms is often minor to negligible for a wide range of k for a variety of classification algorithms and data sets. Furthermore, experimental evidences suggest that the traditional measure of distortion in the community of microdata anonymization may be inappropriate for evaluating the utility of microaggregated data.Postprint (published version

Verification in Privacy Preserving Data Publishing

Privacy preserving data publication is a major concern for both the owners of data and the data publishers. Principles like k-anonymity, l-diversity were proposed to reduce privacy violations. On the other side, no studies were found on verification on the anonymized data in terms of adversarial breach and anonymity levels. However, the anonymized data is still prone to attacks due to the presence of dependencies among quasi-identifiers and sensitive attributes. This paper presents a novel framework to detect the existence of those dependencies and a solution to reduce them. The advantages of our approach are i) privacy violations can be detected, ii) the extent of privacy risk can be measured and iii) re-anonymization can be done on vulnerable blocks of data. The work is further extended to show how the adversarial breach knowledge eventually increased when new tuples are added and an on the fly solution to reduce it is discussed. Experimental results are reported and analyzed