Privacy preservation in mobile social networks

In this day and age with the prevalence of smartphones, networking has evolved in an intricate and complex way. With the help of a technology-driven society, the term "social networking" was created and came to mean using media platforms such as Myspace, Facebook, and Twitter to connect and interact with friends, family, or even complete strangers. Websites are created and put online each day, with many of them possessing hidden threats that the average person does not think about. A key feature that was created for vast amount of utility was the use of location-based services, where many websites inform their users that the website will be using the users' locations to enhance the functionality. However, still far too many websites do not inform their users that they may be tracked, or to what degree. In a similar juxtaposed scenario, the evolution of these social networks has allowed countless people to share photos with others online. While this seems harmless at face-value, there may be times in which people share photos of friends or other non-consenting individuals who do not want that picture viewable to anyone at the photo owner's control. There exists a lack of privacy controls for users to precisely de fine how they wish websites to use their location information, and for how others may share images of them online. This dissertation introduces two models that help mitigate these privacy concerns for social network users. MoveWithMe is an Android and iOS application which creates decoys that move locations along with the user in a consistent and semantically secure way. REMIND is the second model that performs rich probability calculations to determine which friends in a social network may pose a risk for privacy breaches when sharing images. Both models have undergone extensive testing to demonstrate their effectiveness and efficiency.Includes bibliographical reference

A Location-Privacy Approach for Continuous Queries

With the prevalence of smartphones, mobile apps have become more and more popular. However, many mobile apps request location information of the user. If there is nothing in place for location privacy, these mobile app users are in great risk of being tracked by malicious parties. Although the location privacy problem has been studied extensively by resorting to a third-party location anonymizer, there is very little work that allows the users to fully control the disclosure of their data using their smartphones alone. In this paper, we propose a novel Android App called MoveWithMe which automatically generates mocking locations. Most importantly, these mocking locations are not random like those generated by original Android location mocking function. The proposed MoveWithMe app generates k traces of mocking locations and ensures that each trace looks like a trace of a real human and each trace is semantically different from the real user’s trace

Detecting Suspicious File Migration or Replication in the Cloud

There has been a prolific rise in the popularity of cloud storage in recent years. While cloud storage offers many advantages such as flexibility and convenience, users are typically unable to tell or control the actual locations of their data. This limitation may affect users\u27 confidence and trust in the storage provider, or even render cloud unsuitable for storing data with strict location requirements. To address this issue, we propose a system called LAST-HDFS which integrates Location-Aware Storage Technique (LAST) into the open source Hadoop Distributed File System (HDFS). The LAST-HDFS system enforces location-aware file allocations and continuously monitors file transfers to detect potentially illegal transfers in the cloud. Illegal transfers here refer to attempts to move sensitive data outside the ( legal ) boundaries specified by the file owner and its policies. Our underlying algorithms model file transfers among nodes as a weighted graph, and maximize the probability of storing data items of similar privacy preferences in the same region. We equip each cloud node with a socket monitor that is capable of monitoring the real-time communication among cloud nodes. Based on the real-time data transfer information captured by the socket monitors, our system calculates the probability of a given transfer to be illegal. We have implemented our proposed framework and carried out an extensive experimental evaluation in a large-scale real cloud environment to demonstrate the effectiveness and efficiency of our proposed system