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12
Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing
- in Proceedings of the 18th annual international conference on Mobile computing and networking (Mobicom 2012
, 2012
"... Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing a ..."
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Cited by 69 (0 self)
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Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
Providing privacy-aware incentives for mobile sensing
- in Proc. IEEE PerCom
, 2013
"... Abstract—Mobile sensing exploits data contributed by mobile users (e.g., via their smart phones) to make sophisticated inferences about people and their surrounding and thus can be applied to environmental monitoring, traffic monitoring and healthcare. However, the large-scale deployment of mobile s ..."
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Cited by 15 (4 self)
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Abstract—Mobile sensing exploits data contributed by mobile users (e.g., via their smart phones) to make sophisticated inferences about people and their surrounding and thus can be applied to environmental monitoring, traffic monitoring and healthcare. However, the large-scale deployment of mobile sensing applications is hindered by the lack of incentives for users to participate and the concerns on possible privacy leakage. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two privacy-aware incentive schemes for mobile sensing to promote user participation. These schemes allow each mobile user to earn credits by contributing data without leaking which data it has contributed, and at the same time ensure that dishonest users cannot abuse the system to earn unlimited amount of credits. The first scheme considers scenarios where a trusted third party (TTP) is available. It relies on the TTP to protect user privacy, and thus has very low computation and storage cost at each mobile user. The second scheme removes the assumption of TTP and applies blind signature and commitment techniques to protect user privacy. I.
Efficient and Privacy-Preserving Data Aggregation in Mobile Sensing
"... Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple ..."
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Cited by 12 (7 self)
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Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or has high computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggregation protocol to obtain the Min aggregate of timeseries data. Evaluations show that our protocols are orders of magnitude faster than existing solutions. I.
Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error
"... Abstract. Aggregate statistics computed from time-series data contributed by individual mobile nodes can be very useful for many mobile sensing applications. Since the data from individual node may be privacy-sensitive, the aggregator should only learn the desired statistics without compromising the ..."
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Cited by 11 (2 self)
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Abstract. Aggregate statistics computed from time-series data contributed by individual mobile nodes can be very useful for many mobile sensing applications. Since the data from individual node may be privacy-sensitive, the aggregator should only learn the desired statistics without compromising the privacy of each node. To provide strong privacy guarantee, existing approaches add noise to each node’s data and allow the aggregator to get a noisy sum aggregate. However, these approaches either have high computation cost, high communication overhead when nodes join and leave, or accumulate a large noise in the sum aggregate which means high aggregation error. In this paper, we propose a scheme for privacy-preserving aggregation of time-series data in presence of untrusted aggregator, which provides differential privacy for the sum aggregate. It leverages a novel ring-based interleaved grouping technique to efficiently deal with dynamic joins and leaves and achieve low aggregation error. Specifically, when a node joins or leaves, only a small number of nodes need to update their cryptographic keys. Also, the nodes only collectively add a small noise to the sum to ensure differential privacy, which is O(1) with respect to the number of nodes. Based on symmetric-key cryptography, our scheme is very efficient in computation. 1
Efficient and privacy-aware data aggregation in mobile sensing
- IEEE Trans. on Dependable and Secure Computing
, 2014
"... Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple ..."
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Cited by 5 (2 self)
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Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggrega-tion protocol to obtain the Min aggregate of time-series data. To deal with dynamic joins and leaves of mobile users, we propose a scheme which utilizes the redundancy in security to reduce the communication cost for each join and leave. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead. Index Terms—Mobile sensing, privacy, data aggregation I.
Providing Efficient Privacy-Aware Incentives for Mobile Sensing
"... Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively p ..."
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Cited by 2 (2 self)
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Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Existing work on privacy-aware incentive is limited to special scenario of mobile sensing where each sensing task needs only one data report from each user, and thus not appropriate for generic scenarios in which sensing tasks may require multiple reports from each user (e.g., in environmental monitoring applications). In this paper, we propose a privacy-aware incentive scheme for general mobile sensing, which allows each sensing task to collect one or multiple reports from each user as needed. Besides being more flexible in task management, our scheme has much lower computation and communication cost compared to the existing solution. Evaluations show that, when each node only contributes data for a small fraction of sensing tasks (e.g, due to the incapability or disqualification to generate sensing data for other tasks), our scheme runs at least one order of magnitude faster. I.
User-Side Adaptive Protection of Location Privacy in Participatory Sensing
"... Abstract The participatory sensing paradigm, through the growing availability of cheap sensors in mobile devices, enables applications of great social and business interest, e.g., electrosmog exposure measurement and early earthquake detection. However, users ’ privacy concerns regarding their activ ..."
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Abstract The participatory sensing paradigm, through the growing availability of cheap sensors in mobile devices, enables applications of great social and business interest, e.g., electrosmog exposure measurement and early earthquake detection. However, users ’ privacy concerns regarding their activity traces need to be adequately addressed as well. The existing static privacy-enabling approaches, which hide or obfuscate data, offer some protection at the expense of data value. These approaches do not offer privacy guarantees and heterogeneous user privacy requirements cannot be met by them. In this paper, we propose a user-side privacy-protection scheme; it adaptively adjusts its parameters, in order to meet personalized location-privacy protection requirements against adversaries in a measurable manner. As proved by simulation experiments with artificial- and real-data traces, when feasible, our approach not only always satisfies personal location-privacy concerns, but also maximizes data utility (in terms of error, data availability, area coverage), as compared to static privacy-protection schemes.
Providing Privacy-Aware Incentives in Mobile Sensing Systems
"... Abstract-Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively ..."
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Abstract-Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based privacy-aware incentive schemes for mobile sensing systems, where the focus is on privacy protection instead of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and ensure that malicious users cannot abuse the system to earn unlimited credits. Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse attacks. The second scheme considers scenarios where no online TTP is available. It applies blind signature, partially blind signature, and a novel extended Merkle tree technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.
1Providing Privacy-Aware Incentives in Mobile Sensing Systems
"... Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively p ..."
Abstract
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Abstract—Mobile sensing relies on data contributed by users through their mobile device (e.g., smart phone) to obtain useful information about people and their surroundings. However, users may not want to contribute due to lack of incentives and concerns on possible privacy leakage. To effectively promote user participation, both incentive and privacy issues should be addressed. Although incentive and privacy have been addressed separately in mobile sensing, it is still an open problem to address them simultaneously. In this paper, we propose two credit-based privacy-aware incentive schemes for mobile sensing systems, where the focus is on privacy protection instead of on the design of incentive mechanisms. Our schemes enable mobile users to earn credits by contributing data without leaking which data they have contributed, and ensure that malicious users cannot abuse the system to earn unlimited credits. Specifically, the first scheme considers scenarios where an online trusted third party (TTP) is available, and relies on the TTP to protect user privacy and prevent abuse attacks. The second scheme considers scenarios where no online TTP is available. It applies blind signature, partially blind signature, and a novel extended Merkle tree technique to protect user privacy and prevent abuse attacks. Security analysis and cost evaluations show that our schemes are secure and efficient.
