Biblio

Privacy-Preserving Data Mining (PPDM) has become an exciting topic to discuss in recent decades due to the growing interest in big data and data mining. A technique of securing data but still preserving the privacy that is in it. This paper provides an alternative perturbation-based PPDM technique which is carried out by modifying the RNP algorithm. The novelty given in this paper are modifications of some steps method with a specific purpose. The modifications made are in the form of first narrowing the selection of the disturbance value. With the aim that the number of attributes that are replaced in each record line is only as many as the attributes in the original data, no more and no need to repeat; secondly, derive the perturbation function from the cumulative distribution function and use it to find the probability distribution function so that the selection of replacement data has a clear basis. The experiment results on twenty-five perturbed data show that the modified RNP algorithm balances data utility and security level by selecting the appropriate disturbance value and perturbation value. The level of security is measured using privacy metrics in the form of value difference, average transformation of data, and percentage of retains. The method presented in this paper is fascinating to be applied to actual data that requires privacy preservation.

Many organizations are still reluctant to move sensitive data to the cloud. Moreover, data protection regulations have established considerable punishments for violations of privacy and security requirements. Privacy, however, is a concept that is difficult to measure and to demonstrate. While many privacy design strategies, tactics and patterns have been proposed for privacy-preserving system design, it is difficult to evaluate an existing system with regards to whether these strategies have or have not appropriately been implemented. In this paper we propose indicators for a system's non-compliance with privacy design strategies, called privacy smells. To that end we first identify concrete metrics that measure certain aspects of existing privacy design strategies. We then define smells based on these metrics and discuss their limitations and usefulness. We identify these indicators on two levels of a cloud system: the data flow level and the access control level. Using a cloud system built in Microsoft Azure we show how the metrics can be measured technically and discuss the differences to other cloud providers, namely Amazon Web Services and Google Cloud Platform. We argue that while it is difficult to evaluate the privacy-awareness in a cloud system overall, certain privacy aspects in cloud systems can be mapped to useful metrics that can indicate underlying privacy problems. With this approach we aim at enabling cloud users and auditors to detect deep-rooted privacy problems in cloud systems.

With the development of augmented reality(AR) technology and location-based service (LBS) technology, combining AR with LBS will create a new way of life and socializing. In AR, users may consider the privacy and security of data. In LBS, the leakage of user location privacy is an important threat to LBS users. Therefore, it is very important for privacy management of positioning information and user location privacy to avoid loopholes and abuse. In this review, the concepts and principles of AR technology and LBS would be introduced. The existing privacy measurement and privacy protection framework would be analyzed and summarized. Also future research direction of location privacy protection would be discussed.

Microdata is collected by companies in order to enhance their quality of service as well as the accuracy of their recommendation systems. These data often become publicly available after they have been sanitized. Recent reidentification attacks on publicly available, sanitized datasets illustrate the privacy risks involved in microdata collections. Currently, users have to trust the provider that their data will be safe in case data is published or if a privacy breach occurs. In this work, we empower users by developing a novel, user-centric tool for privacy measurement and a new lightweight privacy metric. The goal of our tool is to estimate users' privacy level prior to sharing their data with a provider. Hence, users can consciously decide whether to contribute their data. Our tool estimates an individuals' privacy level based on published popularity statistics regarding the items in the provider's database, and the users' microdata. In this work, we describe the architecture of our tool as well as a novel privacy metric, which is necessary for our setting where we do not have access to the provider's database. Our tool is user friendly, relying on smart visual results that raise privacy awareness. We evaluate our tool using three real world datasets, collected from major providers. We demonstrate strong correlations between the average anonymity set per user and the privacy score obtained by our metric. Our results illustrate that our tool which uses minimal information from the provider, estimates users' privacy levels comparably well, as if it had access to the actual database.