A survey of RFID privacy approaches

A bewildering number of proposals have offered solutions to the privacy problems inherent in RFID communication. This article tries to give an overview of the currently discussed approaches and their attribute

A Survey on Routing in Anonymous Communication Protocols

The Internet has undergone dramatic changes in the past 15 years, and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, ranging from profiling of users for monetizing personal information to nearly omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. Several such systems have been proposed in the literature, each of which offers anonymity guarantees in different scenarios and under different assumptions, reflecting the plurality of approaches for how messages can be anonymously routed to their destination. Understanding this space of competing approaches with their different guarantees and assumptions is vital for users to understand the consequences of different design options. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. To this end, we provide a taxonomy for clustering all prevalently considered approaches (including Mixnets, DC-nets, onion routing, and DHT-based protocols) with respect to their unique routing characteristics, deployability, and performance. This, in particular, encompasses the topological structure of the underlying network; the routing information that has to be made available to the initiator of the conversation; the underlying communication model; and performance-related indicators such as latency and communication layer. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols, and it also helps to clarify the relationship between the routing characteristics of these protocols, and their performance and scalability

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE

The effect of privacy policies on information sharing behavior on social networks: A Systematic Literature Review

Online social networks (OSN) such as Facebook and Instagram have dramatically changed the way people operate. It, however, raises specific privacy concerns due to their inherent handling of personal data. The paper highlights the privacy concerns associated with OSN, strategies to protect the users’ privacy, and finally the overall effect of privacy policies on information sharing behavior on OSN. We examined a sample of 51 full papers that explore privacy concerns in OSN, strategies to protect users’ privacy, and the effects of privacy policies on the users’ information sharing behavior. The overall findings disclosed that users are concerned about their identity being stolen, and how third-party applications use their information. However, privacy policies do not have a direct impact on the information sharing behavior of OSN users. The findings help researchers and practitioners better understand the impact of privacy concern on users\u27 information sharing behaviors on OSN