Search Me If You Can: Privacy-preserving Location Query Service

Location-Based Service (LBS) becomes increasingly popular with the dramatic growth of smartphones and social network services (SNS), and its context-rich functionalities attract considerable users. Many LBS providers use users' location information to offer them convenience and useful functions. However, the LBS could greatly breach personal privacy because location itself contains much information. Hence, preserving location privacy while achieving utility from it is still an challenging question now. This paper tackles this non-trivial challenge by designing a suite of novel fine-grained Privacy-preserving Location Query Protocol (PLQP). Our protocol allows different levels of location query on encrypted location information for different users, and it is efficient enough to be applied in mobile platforms.Comment: 9 pages, 1 figure, 2 tables, IEEE INFOCOM 201

A Marketplace for Efficient and Secure Caching for IoT Applications in 5G Networks

As the communication industry is progressing towards fifth generation (5G) of cellular networks, the traffic it carries is also shifting from high data rate traffic from cellular users to a mixture of high data rate and low data rate traffic from Internet of Things (IoT) applications. Moreover, the need to efficiently access Internet data is also increasing across 5G networks. Caching contents at the network edge is considered as a promising approach to reduce the delivery time. In this paper, we propose a marketplace for providing a number of caching options for a broad range of applications. In addition, we propose a security scheme to secure the caching contents with a simultaneous potential of reducing the duplicate contents from the caching server by dividing a file into smaller chunks. We model different caching scenarios in NS-3 and present the performance evaluation of our proposal in terms of latency and throughput gains for various chunk sizes

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users\u27 location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle\u27s trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers\u27 control on location privacy without affecting the utility of UBI. Our defense framework can also detect users\u27 dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201