z-anonymity: Zero-Delay Anonymization for Data Streams

With the advent of big data and the birth of the data markets that sell personal information, individuals' privacy is of utmost importance. The classical response is anonymization, i.e., sanitizing the information that can directly or indirectly allow users' re-identification. The most popular solution in the literature is the k-anonymity. However, it is hard to achieve k-anonymity on a continuous stream of data, as well as when the number of dimensions becomes high.In this paper, we propose a novel anonymization property called z-anonymity. Differently from k-anonymity, it can be achieved with zero-delay on data streams and it is well suited for high dimensional data. The idea at the base of z-anonymity is to release an attribute (an atomic information) about a user only if at least z - 1 other users have presented the same attribute in a past time window. z-anonymity is weaker than k-anonymity since it does not work on the combinations of attributes, but treats them individually. In this paper, we present a probabilistic framework to map the z-anonymity into the k-anonymity property. Our results show that a proper choice of the z-anonymity parameters allows the data curator to likely obtain a k-anonymized dataset, with a precisely measurable probability. We also evaluate a real use case, in which we consider the website visits of a population of users and show that z-anonymity can work in practice for obtaining the k-anonymity too

Practical anonymization for data streams: z-anonymity and relation with k-anonymity

With the advent of big data and the emergence of data markets, preserving individuals’ privacy has become of utmost importance. The classical response to this need is anonymization, i.e., sanitizing the information that, directly or indirectly, can allow users’ re-identification. Among the various approaches, -anonymity provides a simple and easy-to-understand protection. However, -anonymity is challenging to achieve in a continuous stream of data and scales poorly when the number of attributes becomes high. In this paper, we study a novel anonymization property called -anonymity that we explicitly design to deal with data streams, i.e., where the decision to publish a given attribute (atomic information) is made in real time. The idea at the base of -anonymity is to release such attribute about a user only if at least other users have exposed the same attribute in a past time window. Depending on the value of , the output stream results -anonymized with a certain probability. To this end, we present a probabilistic model to map the -anonymity into the -anonymity property. The model is not only helpful in studying the -anonymity property, but also general enough to evaluate the probability of achieving -anonymity in data streams, resulting in a generic contribution

An iot-based anonymous function for security and privacy in healthcare sensor networks

Funding Information: Funding: This research was supported by the Scientific Fund Project of Facility Horticulture Laboratory of Universities in Shandong of China (Grant number: 2018YY016) and the Doctoral Scientific Fund Project of Weifang University of Science & Technology of China (Grant number: 2017BS17), it was also supported by the Innovation Fund of Ministry of Education, Science and Technology Development Center of China (Grant number: 2018A02013).Peer reviewe

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance