Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness

A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets

When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference

Renyi Differential Privacy

We propose a natural relaxation of differential privacy based on the Renyi divergence. Closely related notions have appeared in several recent papers that analyzed composition of differentially private mechanisms. We argue that the useful analytical tool can be used as a privacy definition, compactly and accurately representing guarantees on the tails of the privacy loss. We demonstrate that the new definition shares many important properties with the standard definition of differential privacy, while additionally allowing tighter analysis of composite heterogeneous mechanisms

Adaptable Privacy-preserving Model

Current data privacy-preservation models lack the ability to aid data decision makers in processing datasets for publication. The proposed algorithm allows data processors to simply provide a dataset and state their criteria to recommend an xk-anonymity approach. Additionally, the algorithm can be tailored to a preference and gives the precision range and maximum data loss associated with the recommended approach. This dissertation report outlined the research’s goal, what barriers were overcome, and the limitations of the work’s scope. It highlighted the results from each experiment conducted and how it influenced the creation of the end adaptable algorithm. The xk-anonymity model built upon two foundational privacy models, the k-anonymity and l-diversity models. Overall, this study had many takeaways on data and its power in a dataset

Data Anonymization Using Map Reduce on Cloud based A Scalable Two-Phase Top-Down Specialization

A large number of cloud services require users to impart` private data like electronic health records for data analysis or Mining, bringing privacy concerns. Anonymizing information sets through generalization to fulfill certain security prerequisites, for example, k-anonymity is a broadly utilized classification of protection safeguarding procedures At present, the scale of information in numerous cloud applications increments immensely as per the Big Data pattern, in this manner making it a test for normally utilized programming instruments to catch, oversee, and process such substantial scale information inside a bearable slipped by time. As an issue, it is a test for existing anonymization methodologies to accomplish security protection on security touchy extensive scale information sets because of their inadequacy of adaptability. In this paper, we propose a versatile two-stage top-down specialization (TDS) methodology to anonymize huge scale information sets utilizing the Map reduce schema on cloud. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches

A survey on security and privacy issues in IoV

As an up-and-coming branch of the internet of things, internet of vehicles (IoV) is imagined to fill in as a fundamental information detecting and processing platform for astute transportation frameworks. Today, vehicles are progressively being associated with the internet of things which empower them to give pervasive access to data to drivers and travelers while moving. Be that as it may, as the quantity of associated vehicles continues expanding, new prerequisites, (for example, consistent, secure, vigorous, versatile data trade among vehicles, people, and side of the road frameworks) of vehicular systems are developing. Right now, the unique idea of vehicular specially appointed systems is being changed into another idea called the internet of vehicles (IoV). We talk about the issues faced in implementing a secure IoV architecture. We examine the various challenges in implementing security and privacy in IoV by reviewing past papers along with pointing out research gaps and possible future work and putting forth our on inferences relating to each paper

Data Anonymization for Privacy Preservation in Big Data

Cloud computing provides capable ascendable IT edifice to provision numerous processing of a various big data applications in sectors such as healthcare and business. Mainly electronic health records data sets and in such applications generally contain privacy-sensitive data. The most popular technique for data privacy preservation is anonymizing the data through generalization. Proposal is to examine the issue against proximity privacy breaches for big data anonymization and try to recognize a scalable solution to this issue. Scalable clustering approach with two phase consisting of clustering algorithm and K-Anonymity scheme with Generalisation and suppression is intended to work on this problem. Design of the algorithms is done with MapReduce to increase high scalability by carrying out dataparallel execution in cloud. Wide-ranging researches on actual data sets substantiate that the method deliberately advances the competence of defensive proximity privacy breaks, the scalability and the efficiency of anonymization over existing methods. Anonymizing data sets through generalization to gratify some of the privacy attributes like k- Anonymity is a popularly-used type of privacy preserving methods. Currently, the gauge of data in numerous cloud surges extremely in agreement with the Big Data, making it a dare for frequently used tools to actually get, manage, and process large-scale data for a particular accepted time scale. Hence, it is a trial for prevailing anonymization approaches to attain privacy conservation for big data private information due to scalabilty issues