Towards new methods for mobility data gathering: content, sources, incentives

Over the past decade, huge amounts of work has been done in mobile and opportunistic networking research. Unfortunately, much of this has had little impact as the results have not been applicable to reality, due to incorrect assumptions and models used in the design and evaluation of the systems. In this paper, we outline some of the problems of the assumptions of early research in the field, and provide a survey of some initial work that has started to take place to alleviate this through more realistic modelling and measurements of real systems. We do note that there is still much work to be done in this area, and then go on to identify some important properties of the network that must be studied further. We identify the types of data that are important to measure, and also give some guidelines on finding existing and potentially new sources for such data and incentivizing the holders of the data to share it

Opportunistic Sensing: Security Challenges for the New Paradigm

We study the security challenges that arise in Opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm

ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs

The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal

Understanding Shoulder Surfing in the Wild: Stories from Users and Observers

Research has brought forth a variety of authentication systems to mitigate observation attacks. However, there is little work about shoulder surfing situations in the real world. We present the results of a user survey (N=174) in which we investigate actual stories about shoulder surfing on mobile devices from both users and observers. Our analysis indicates that shoulder surfing mainly occurs in an opportunistic, non-malicious way. It usually does not have serious consequences, but evokes negative feelings for both parties, resulting in a variety of coping strategies. Observed data was personal in most cases and ranged from information about interests and hobbies to login data and intimate details about third persons and relationships. Thus, our work contributes evidence for shoulder surfing in the real world and informs implications for the design of privacy protection mechanisms

AnonySense: A System for Anonymous Opportunistic Sensing

We describe AnonySense, a privacy-aware system for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing \emphtasks\/ to be distributed across participating mobile devices, later receiving verified, yet anonymized, sensor data \emphreports\/ back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our security goals, threat model, and the architecture and protocols of AnonySense. We also describe how AnonySense can support extended security features that can be useful for different applications. We evaluate the security and feasibility of AnonySense through security analysis and prototype implementation. We show the feasibility of our approach through two plausible applications: a Wi-Fi rogue access point detector and a lost-object finder