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In order to facilitate the sharing and dissemination of public data, the open data concept has been developed in recent years. Apart from its unquestionable positive effects, the whole process of opening data can also lead to negative ones. In most of the sectors processing vast amounts of data, such as medical, financial, or environmental, there are also legally protected data, the publication of which has far-reaching pejorative consequences. The article presents selected methods for sharing data containing confidential information in open data systems. The depersonalization of data subject to modifications makes it impossible to identify the subject that has been moved. Anonymization and pseudo-anonymization techniques were used. The most popular methods of data anonymization were compared: masking, permutations, adding noise, k-anonymization, l-diversification, and t-closeness. The risk of data extraction, correlation, or reasoning was determined based on the research.

The modern technology in today’s world is largely dependent on data. At times, data proves to be more valuable than money. Data constitutes information and this information can help to keep peace or even cause wars. Impact of data has increased with the skyrocketing of artificial intelligence, machine learning, and deep learning techniques where data is the main food. Consequently, privacy of data has also become an important aspect to be taken care of from potential hackers and unscrupulous persons. Healthcare sector is a sensitive area often targeted by hackers. Various anonymization techniques like k- anonymization, l-diversity, and t-closeness contribute in maintaining the data privacy thereby securing it. We propose another anonymization technique namely single identity clustering which is inspired from the t-closeness method. Our proposed method enhances the data privacy that is already provided using the existing anonymization techniques.

In the age of big data, data is increasing in a tremendous way in many fields and the data shared by the users is in a great risk. To preserve privacy of an individual anonymization-based algorithm like k-anonymity-related algorithm and differential privacy is proposed to make sure that the resulting dataset is free from privacy disclosure. However, majority of these anonymization algorithms are applied in isolated environment, without considering the utility in knowledge task making the dataset less informative. Also the presence of redundant data also decreases the performance and reduces accuracy of anonymization. Hence a preprocessing-based anonymization is required to increase the utility and to achieve accuracy in anonymization. This paper aims to apply the feature selection fast correlation-based filter (FCBF) solution to select the relevant features and remove the redundant data. Then k-anonymity is applied to dataset to achieve data anonymization. Comparisons on real-world dataset were made with anonymized dataset with preprocessing and without preprocessing and result was produced.

In this chapter, we introduce data anonymization techniques for several types of datasets. Data anonymity of anonymized datasets is an index for estimating the (maximum) reidentification risk from anonymized datasets and is generally defined as a quantitative index based on adversary models. The adversary models are implicitly defined according to the attributes in the datasets, use cases, and anonymization techniques. We first review existing anonymization techniques and the adversary models behind the data anonymity definitions for anonymization techniques; then, we propose a common anonymity definition and its adversary model, which is applicable to several types of anonymization techniques. Furthermore, some extensions of the definition, which is optimized for specific types of datasets, are presented in the chapter.

Privacy preservation is a serious concern of today’s scenario. Anonymity is a way to preserve privacy of different types of data publications like structural and descriptive. In this paper, we discuss the preservation of both types of data. We use anatomy for descriptive data and sequential clustering for structural data anonymization. The proposed approach is based on assortativity called Anatomy Based Modified Sequential Clustering (ABMSC). We propose a new total info. loss measure for cluster optimization. The performance of the proposed approach is measured by information loss and number of node movements takes place to meet the optimization. The experimental results show that our proposed algorithm outperforms the two state-of-the-art algorithms.

Modern technology produces a large number of public and private data sets that make the task of securing personal data unavoidable. Initially, priority was given to securing data for organizations and companies, but nowadays it is also necessary to provide security for personal data. Therefore, to achieve information security, the protection of individual data is critical. Data anonymization is a technique for preserving privacy in data publishing, which enables the publication of practically useful information for data mining while preserving the confidentiality of the information of the individual. This chapter presents the implementation of data anonymization using a proposed improved k-anonymity algorithm applied to a large candidate election data set acquired from the Madhya Pradesh (MP, India) State Election Commission. Along with greater privacy, the algorithm is executed in less time than the traditional k-anonymity algorithm, and as such, it is able to satisfy the data protection needs of the current big data environment.

Nowadays, Cloud computing becomes a looming computing prototype. Users can get a variety of services such as high computation power, storage, etc. Thus, applications of users can be more cost-effectively put on cloud by utilizing various commodity computers together. But, cloud computing faces some security concerns even if they provides many services. Some of such important concerns are data security and privacy of data. Some personal data like personal healthcare records and financial records contain sensitive information which can be analyzed and mined for public researches although these records offer important human assets. Data should be privacy preserved because malicious cloud users or untrusted cloud providers can get the data with less effort. To deal with these problems, privacy-aware set-valued data publishing on cloud for personal healthcare records has been proposed. An efficient privacy-aware system, named PHKEM (Personal Healthcare k-anonymity Encryption Model), for eliminating privacy breaches in publishing of personal healthcare data on cloud as well as data querying is designed. A data anonymization technique, named k-Anonymity with extended quasi-identifier partitioning (EQI-partitioning), interactive differential privacy, and AES encryption is applied to preserving personal healthcare records to prevent unauthorized access. Therefore, the security is efficiently enhanced with a natural and expressive fashion.

The Internet of Things (IoT) is the future of Internet where users, machines and everyday Things have the ability to sense, communicate and interact with their environment. IoT promises a wide variety of applications to make the human’s life more comfortable, safe and improve quality of life. It’s also considered as big business opportunity for enterprises based on huge quantity of data gathered by connected Things. In the IoT applications such as smart home, smart city, heath and so on, Things cohabit with humans and deal with their personal data even the most private ones. When this data is collected massively and exposed to Internet without an explicit person’s agreement, Things constitute by this way a threat for privacy, which is a universal human right. Thus privacy preservation is one of the most prominent issues in IoT. This paper analyzes the privacy in the context of IoT based on case study, and proposes mechanisms to improve security and preserve privacy. This analysis considers also the economic advantages related to the use of IoT as new business opportunity without personal private data disclosure. The proposed solutions are based on data anonymity technologies with recommendation for users and for developers of IoT application.