FLIP: A Utility Preserving Privacy Mechanism for Time Series

Guaranteeing privacy in released data is an important goal for data-producing agencies. There has been extensive research on developing suitable privacy mechanisms in recent years. Particularly notable is the idea of noise addition with the guarantee of differential privacy. There are, however, concerns about compromising data utility when very stringent privacy mechanisms are applied. Such compromises can be quite stark in correlated data, such as time series data. Adding white noise to a stochastic process may significantly change the correlation structure, a facet of the process that is essential to optimal prediction. We propose the use of all-pass filtering as a privacy mechanism for regularly sampled time series data, showing that this procedure preserves utility while also providing sufficient privacy guarantees to entity-level time series.Comment: 19 pages, 5 figure

Review of Social Networking Sites' Security and Privacy

Nowadays social media networking has dramatically increased. Social networking sites like Facebook make users create huge amount of profiles and share personal information within networking of different users. Social networking exposes personal information far beyond the group of friends. And that information or data on social media networking could be potential threat to people's information security and privacy. In this review, we are going to view the privacy risks and security problems of social media websites. We also present the types of potential security attacks and how they are made. We will show the basic security requirements for social media networking and present some useful security solutions from both personal users and networking experts

Improving a wireless localization system via machine learning techniques and security protocols

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community

Differentially Private Event Stream Filtering with an Application to Traffic Estimation

RÉSUMÉ Beaucoup de systèmes à grande échelle tels que les systèmes de transport intelligents, les réseaux intelligents ou les bâtiments intelligents requièrent que des individus contribuent leurs flux de données privées afin d’amasser, stocker, manipuler et analyser les informations pour le traitement du signal et à des fins de prise de décision. Dans un scénario typique, un essaim de capteurs produit des signaux d’entrée à valeurs discrètes décrivant l’occurrence d’événements relatifs à ces individus. En conséquence, des statistiques utiles doivent être publiées continuellement et en temps réel. Cependant, cela peut engendrer une perte de confidentialité pour les utilisateurs. Cette thèse considère le problème de fournir des garanties de confidentialité différentielle pour ces systèmes multi-sorties multi-entrées fonctionnant en continu. En particulier, nous considérons la question de confidentialité dans le contexte de la théorie des systèmes et nous étudions le problème de génération de signaux qui respectent la confidentialité des utilisateurs qui activent les capteurs. Nous présentons une nouvelle architecture d’estimation des flux de trafic préservant la confidentialité des conducteurs. Nous introduisons aussi une surveillance différentiellement confidentielle d’occupation dans un bâtiment équipé d’un dense réseau de capteurs de détection de mouvement, qui sera utile par exemple pour commander le système HVAC.----------ABSTRACT Many large-scale systems such as intelligent transportation systems, smart grids or smart buildings require individuals to contribute their private data streams in order to amass, store, manipulate and analyze information for signal processing and decision-making purposes. In a typical scenario, swarms of sensors produce discrete-valued input signals that describe the occurrence of events involving these users and several statistics of interest need to be continuously published in real-time. This can however engender a privacy loss for the users in exchange of the utility provided by the application. This thesis considers the problem of providing dierential privacy guarantees for such multi-input multi-output systems operating continuously. In particular, we consider the privacy issues in a system theoretic context, and address the problem of releasing filtered signals that respect the privacy of users who activate the sensors. As a result of this thesis we present a new architecture for privacy preserving estimation of trac flows. We also introduce dierentially private monitoring and forecasting occupancy in a building equipped with a dense network of motion detection sensors, which is useful for example to control its HVAC system

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved