869 research outputs found
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
Game Theory Based Privacy Protection for Context-Aware Services
In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information
Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions
abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
The recent proliferation of increasingly capable mobile devices has given
rise to mobile crowd sensing (MCS) systems that outsource the collection of
sensory data to a crowd of participating workers that carry various mobile
devices. Aware of the paramount importance of effectively incentivizing
participation in such systems, the research community has proposed a wide
variety of incentive mechanisms. However, different from most of these existing
mechanisms which assume the existence of only one data requester, we consider
MCS systems with multiple data requesters, which are actually more common in
practice. Specifically, our incentive mechanism is based on double auction, and
is able to stimulate the participation of both data requesters and workers. In
real practice, the incentive mechanism is typically not an isolated module, but
interacts with the data aggregation mechanism that aggregates workers' data.
For this reason, we propose CENTURION, a novel integrated framework for
multi-requester MCS systems, consisting of the aforementioned incentive and
data aggregation mechanism. CENTURION's incentive mechanism satisfies
truthfulness, individual rationality, computational efficiency, as well as
guaranteeing non-negative social welfare, and its data aggregation mechanism
generates highly accurate aggregated results. The desirable properties of
CENTURION are validated through both theoretical analysis and extensive
simulations
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Exploring Societal Computing based on the Example of Privacy
Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. This thesis will consist of the following four projects that aim to address the issues of privacy and software engineering.
First, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate their privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and their personal data such as location than about their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives.
Second, a related challenge to above, is to make privacy more understandable in complex systems that may have a variety of user interface options, which may change often. As social network platforms have evolved, the ability for users to control how and with whom information is being shared introduces challenges concerning the configuration and comprehension of privacy settings. To address these concerns, our crowd sourced approach simplifies the understanding of privacy settings by using data collected from 512 users over a 17 month period to generate visualizations that allow users to compare their personal settings to an arbitrary subset of individuals of their choosing. To validate our approach we conducted an online survey with closed and open questions and collected 59 valid responses after which we conducted follow-up interviews with 10 respondents. Our results showed that 70% of respondents found visualizations using crowd sourced data useful for understanding privacy settings, and 80% preferred a crowd sourced tool for configuring their privacy settings over current privacy controls.
Third, as software evolves over time, this might introduce bugs that breach users' privacy. Further, there might be system-wide policy changes that could change users' settings to be more or less private than before. We present a novel technique that can be used by end-users for detecting changes in privacy, i.e., regression testing for privacy. Using a social approach for detecting privacy bugs, we present two prototype tools. Our evaluation shows the feasibility and utility of our approach for detecting privacy bugs. We highlight two interesting case studies on the bugs that were discovered using our tools. To the best of our knowledge, this is the first technique that leverages regression testing for detecting privacy bugs from an end-user perspective.
Fourth, approaches to addressing these privacy concerns typically require substantial extra computational resources, which might be beneficial where privacy is concerned, but may have significant negative impact with respect to Green Computing and sustainability, another major societal concern. Spending more computation time results in spending more energy and other resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable - systems where privacy could be achieved "for free", i.e., without having to spend extra computational effort. We describe how privacy can indeed be achieved for free an accidental and beneficial side effect of doing some existing computation - in web applications and online systems that have access to user data. We show the feasibility, sustainability, and utility of our approach and what types of privacy threats it can mitigate.
Finally, we generalize the problem of privacy and its tradeoffs. As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole
IoT Security Evolution: Challenges and Countermeasures Review
Internet of Things (IoT) architecture, technologies, applications and security have been recently addressed by a number of researchers. Basically, IoT adds internet connectivity to a system of intelligent devices, machines, objects and/or people. Devices are allowed to automatically collect and transmit data over the Internet, which exposes them to serious attacks and threats. This paper provides an intensive review of IoT evolution with primary focusing on security issues together with the proposed countermeasures. Thus, it outlines the IoT security challenges as a future roadmap of research for new researchers in this domain
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