Biblio

Continuous monitoring and risk assessment of privacy violations on cloud systems are needed by anyone who has business needs subject to privacy regulations. Compliance to such regulations in dynamic systems demands appropriate techniques, tools and instruments. As a Service concepts can be a good option to support this task. Previous work presented PRIVAaaS, a software toolkit that allows controlling and reducing data leakages, thus preserving privacy, by providing anonymization capabilities to query-based systems. This short paper discusses the implementation details and deployment environment of an evolution of PRIVAaaS as a MAPE-K control loop within the ATMOSPHERE Platform. ATMOSPHERE is both a framework and a platform enabling the implementation of trustworthy cloud services. By enabling PRIVAaaS within ATMOSPHERE, privacy is made one of several trustworthiness properties continuously monitored and assessed by the platform with a software-based, feedback control loop known as MAPE-K.

Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.

Online Social Networks(OSN) plays a vital role in our day to day life. The most popular social network, Facebook alone counts currently 2.23 billion users worldwide. Online social network users are aware of the various security risks that exist in this scenario including privacy violations and they are utilizing the privacy settings provided by OSN providers to make their data safe. But most of them are unaware of the risk which exists after deletion of their data which is not really getting deleted from the OSN server. Self destruction of data is one of the prime recommended methods to achieve assured deletion of data. Numerous techniques have been developed for self destruction of data and this paper discusses and evaluates these techniques along with the various privacy risks faced by an OSN user in this web centered world.

The increasing publication of large amounts of data, theoretically anonymous, can lead to a number of attacks on the privacy of people. The publication of sensitive data without exposing the data owners is generally not part of the software developers concerns. The regulations for the data privacy-preserving create an appropriate scenario to focus on privacy from the perspective of the use or data exploration that takes place in an organization. The increasing number of sanctions for privacy violations motivates the systematic comparison of three known machine learning algorithms in order to measure the usefulness of the data privacy preserving. The scope of the evaluation is extended by comparing them with a known privacy preservation metric. Different parameter scenarios and privacy levels are used. The use of publicly available implementations, the presentation of the methodology, explanation of the experiments and the analysis allow providing a framework of work on the problem of the preservation of privacy. Problems are shown in the measurement of the usefulness of the data and its relationship with the privacy preserving. The findings motivate the need to create optimized metrics on the privacy preferences of the owners of the data since the risks of predicting sensitive attributes by means of machine learning techniques are not usually eliminated. In addition, it is shown that there may be a hundred percent, but it cannot be measured. As well as ensuring adequate performance of machine learning models that are of interest to the organization that data publisher.

The rapid development of the Internet of Things (IoT) results in generating massive amounts of data. Significant portions of these data are sensitive since they reflect (directly or indirectly) peoples' behaviors, interests, lifestyles, etc. Protecting sensitive IoT data from privacy violations is a challenge since these data need to be communicated, processed, analyzed, and stored by public networks, servers, and clouds; most of them are untrusted parties for data owners. We propose a solution for protecting sensitive IoT data called Policy Enforcement Fog Module (PEFM). The major task of the PEFM solution is mandatory enforcement of privacy policies for sensitive IoT data-wherever these data are accessed throughout their entire lifecycle. The key feature of PEFM is its placement within the fog computing infrastructure, which assures that PEFM operates as closely as possible to data sources within the edge. PEFM enforces policies directly for local IoT applications. In contrast, for remote applications, PEFM provides a self-protecting mechanism based on creating and disseminating Active Data Bundles (ADBs). ADBs are software constructs bundling inseparably sensitive data, their privacy policies, and an execution engine able to enforce privacy policies. To prove effectiveness and efficiency of the proposed module, we developed a smart home proof-of-concept scenario. We investigate privacy threats for sensitive IoT data. We run simulation experiments, based on network calculus, for testing performance of the PEFM controls for different network configurations. The results of the simulation show that-even with using from 1 to 5 additional privacy policies for improved data privacy-penalties in terms of execution time and delay are reasonable (approx. 12-15% and 13-19%, respectively). The results also show that PEFM is scalable regarding the number of the real-time constraints for real-time IoT applications.