Xuan Ding

A Brief Survey on De-anonymization Attacks in Online Social Networks

Nowadays, online social network data are increasingly made publicly available to third parties. Several anonymization techniques have been studied and adopted to preserve privacy in the publishing of data. However, recent works have shown that de-anonymization of the released data is not only possible but also practical. In this paper, we present a brief yet systematic review of the existing deanonymization attacks in online social networks. We unify the models of de-anonymization, centering around the concept of feature matching. We survey the de-anonymization methods in two categories: mapping-based approaches and guessing-based approaches. We discuss three techniques that would potentially improve the surveyed attacks.

POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices

Facing a large number of personal photos and limited resource of mobile devices, cloud plays an important role in photo storing, sharing and searching. Meanwhile, some recent reputation damage and stalk events caused by photo leakage increase peoples concern about photo privacy. Though most would agree that photo search function and privacy are both valuable, few cloud system supports both of them simultaneously. The center of such an ideal system is privacy-preserving outsourced image similarity measurement, which is extremely challenging when the cloud is untrusted and a high extra overhead is disliked. In this work, we introduce a framework POP, which enables privacy-seeking mobile device users to outsource burdensome photo sharing and searching safely to untrusted servers. Unauthorized parties, including the server, learn nothing about photos or search queries. This is achieved by our carefully designed architecture and novel non-interactive privacy-preserving protocols for image similarity computation. Our framework is compatible with the state-of-the-art image search techniques, and it requires few changes to existing cloud systems. For efficiency and good user experience, our framework allows users to define personalized private content by a simple check-box configuration and then enjoy the sharing and searching services as usual. All privacy protection modules are transparent to users. The evaluation of our prototype implementation with 31,772 real-life images shows little extra communication and computation overhead caused by our system.

WiFace: a secure geosocial networking system using WiFi-based multi-hop MANET

A number of mobile online social networking (OSN) services appear in the market. Majority of mobile systems can strongly benefit from services offered by cloud. However, centralized servers and communication infrastructures may not always be available. Further their location-based services are not offered to low-end mobile devices without GPS modules. To build a system that can take advantage of cloud, and also can address these potential problems that could hinder OSN usage, we design and construct a multi-hop networking system named MoNet based on WiFi, and on top of which we design and implement WiFace, a privacy-aware geosocial networking service. For the situation without any infrastructure, we design a distributed content sharing protocol which can significantly shorten the relay path, reduce conflicts and improve data persistence and availability. A role strategy is designed to encourage users to collaborate in the network. Furthermore, a key management and an authorization mechanism are developed to prevent some attacks and protect privacy. We conduct comprehensive experiments to evaluate the performance of our mobile platform MoNet and application WiFace. The results show that MoNet is more than sufficient to support social networking, and even audio and video applications.

Privacy-friendly photo capturing and sharing system

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases peoples concern on privacy infringement. Here we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their requirements. To make this work, we need to address three challenges: 1) how to enable users explicitly express their privacy protection intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

PIC: Enable Large-Scale Privacy Preserving Content-Based Image Search on Cloud

Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., people, location and event) and people’s privacy concerns hinder their participation into untrusted services, today’s cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: A Privacy-preserving Image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others’ images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.

Demo: high-precision RFID tracking using COTS devies

In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. We achieve these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tags moving trajectory and its locations, relaxing the assumption of knowing tags track function in advance.