Part 10: PrivacyInternational audienceReputation systems are crucial for distributed applications in which users have to be made accountable for their actions, such as e-commerce websites. However, existing systems often disclose the identity of the raters, which might deter honest users from submitting reviews out of fear of retaliation from the ratees. While many privacy-preserving reputation systems have been proposed, we observe that none of them is simultaneously truly decentralized, trustless, and suitable for real world usage in, for example, e-commerce applications. In this paper, we present a blockchain based decentralized privacy-preserving reputation system. We demonstrate that our system provides correctness and security while eliminating the need for users to trust any third parties or even fellow users

The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting directions to work in the morning, leaving a check-in at a restaurant at noon and checking next day's weather in the evening are possible right from any mobile device embedding a GPS chip. In these location-based applications, the user's location is sent to a server, which uses them to provide contextual and personalised answers. However, nothing prevents the latter from gathering, analysing and possibly sharing the collected information, which opens the door to many privacy threats. Indeed, mobility data can reveal sensitive information about users, among which one's home, work place or even religious and political preferences. For this reason, many privacy-preserving mechanisms have been proposed these last years to enhance location privacy while using geolocated services. This article surveys and organises contributions in this area from classical building blocks to the most recent developments of privacy threats and location privacy-preserving mechanisms. We divide the protection mechanisms between online and offline use cases, and organise them into six categories depending on the nature of their algorithm. Moreover, this article surveys the evaluation metrics used to assess protection mechanisms in terms of privacy, utility and performance. Finally, open challenges and new directions to address the problem of computational location privacy are pointed out and discussed.