Improve User Privacy Research Articles

Improving App Privacy: Nudging App Developers to Protect User Privacy

Smartphone app developers make many privacy-related decisions on what data to collect about users and how that data is used. Based on interviews and a survey of app developers, the authors identify several hurdles preventing app developers from improved privacy behaviors. These include the difficulties of reading and writing privacy policies as well as privacy not being their primary task. The authors suggest some nudges that would help app developers improve user privacy as well as public policy focus on incentivizing all players in the app development ecosystem to help developers implement better privacy behaviors.

Friend or foe? Fake profile identification in online social networks

The amount of personal information involuntarily exposed by users on online social networks is staggering, as shown in recent research. Moreover, recent reports indicate that these networks are inundated with tens of millions of fake user profiles, which may jeopardize the user’s security and privacy. To identify fake users in such networks and to improve users’ security and privacy, we developed the Social Privacy Protector (SPP) software for Facebook. This software contains three protection layers that improve user privacy by implementing different methods to identify fake profiles. The first layer identifies a user’s friends who might pose a threat and then restricts the access these “friends” have to the user’s personal information. The second layer is an expansion of Facebook’s basic privacy settings based on different types of social network usage profiles. The third layer alerts users about the number of installed applications on their Facebook profile that has access to their private information. An initial version of the SPP software received positive media coverage, and more than 3,000 users from more than 20 countries have installed the software, out of which 527 have used the software to restrict more than 9,000 friends. In addition, we estimate that more than 100 users have accepted the software’s recommendations and removed nearly 1,800 Facebook applications from their profiles. By analyzing the unique dataset obtained by the software in combination with machine learning techniques, we developed classifiers that are able to predict Facebook profiles with a high probability of being fake and consequently threaten the user’s security and privacy. Moreover, in this study, we present statistics generated by the SPP software on both user privacy settings and the number of applications installed on Facebook profiles. These statistics alarmingly demonstrate how vulnerable Facebook users’ information is to both fake profile attacks and third-party Facebook applications.

The cloud storage ciphertext retrieval scheme based on ORAM

Due to its characteristics distribution and virtualization, cloud storage also brings new security problems. User's data is stored in the cloud, which separated the ownership from management. How to ensure the security of cloud data, how to increase data availability and how to improve user privacy perception are the key issues of cloud storage research, especially when the cloud service provider is not completely trusted. In this paper, a cloud storage ciphertext retrieval scheme based on AES and homomorphic encryption is presented. This ciphertext retrieval scheme will not only conceal the user retrieval information, but also prevent the cloud from obtaining user access pattern such as read-write mode, and access frequency, thereby ensuring the safety of the ciphertext retrieval and user privacy. The results of simulation analysis show that the performance of this ciphertext retrieval scheme requires less overhead than other schemes on the same security level.

Privacy-preserving P2P data sharing with OneSwarm

Privacy -- the protection of information from unauthorized disclosure -- is increasingly scarce on the Internet. The lack of privacy is particularly true for popular peer-to-peer data sharing applications such as BitTorrent where user behavior is easily monitored by third parties. Anonymizing overlays such as Tor and Freenet can improve user privacy, but only at a cost of substantially reduced performance. Most users are caught in the middle, unwilling to sacrifice either privacy or performance. In this paper, we explore a new design point in this tradeoff between privacy and performance. We describe the design and implementation of a new P2P data sharing protocol, called OneSwarm, that provides users much better privacy than BitTorrent and much better performance than Tor or Freenet. A key aspect of the OneSwarm design is that users have explicit configurable control over the amount of trust they place in peers and in the sharing model for their data: the same data can be shared publicly, anonymously, or with access control, with both trusted and untrusted peers. OneSwarm's novel lookup and transfer techniques yield a median factor of 3.4 improvement in download times relative to Tor and a factor of 6.9 improvement relative to Freenet. OneSwarm is publicly available and has been downloaded by hundreds of thousands of users since its release.