Anonymous communication, that is secure end-to-end and unlinkable, plays a critical role in protecting user privacy by preventing service providers from using message metadata to discover communication links between any two users. Techniques, such as Mix-net, DC-net, time delay, cover traffic, Secure Multiparty Computation (SMC) and Private Information Retrieval, can be used to achieve anonymous communication. SMC-based approach generally offers stronger simulation based security guarantee. In this paper, we propose a simple and novel SMC approach to establishing anonymous communication, easily implementable with two non-colluding servers which have only communication and storage related capabilities. Our approach offers stronger security guarantee against malicious adversaries without incurring a great deal of extra computation. To show its practicality, we implemented our solutions using Chameleon Cloud to simulate the interactions among a million users, and extensive simulations were conducted to show message latency with various group sizes. Our approach is efficient for smaller group sizes and sub-group communication while preserving message integrity. Also, it does not have the message collision problem.

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' 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.

There has been a prolific rise in the popularityof cloud storage in recent years. While cloud storage offersmany advantages such as flexibility and convenience, users arenow unable to tell or control the actual locations of their data. This limitation may affect users' confidence and trust in thestorage provider, or even be unsuitable for storing data withstrict location requirements. To address this issue, we proposean illegal file transfer detection framework that constantlymonitors the real-time file transfers in the cloud and is capableof detecting potential illegal transfers which moves sensitivedata outside the ("legal") boundaries specified by the fileowner. The main idea is to classifying multiple users' location preferences when making the data storage arrangement inthe cloud nodes. We model the legal file transfers amongnodes as a weighted graph and then maximize the probabilityof storing data items of similar privacy preferences in thesame region. Then we leverage the socket monitoring functionsprovided by LAST-HDFS (a recent location-aware Hadoop filestorage system) to monitor the real-time communication amongcloud nodes. Based on our legal file transfer graph and thedetected communication, we propose an approach to calculatethe probability of the detected transfer to be illegal.