Learning the Truth Privately and Confidently: Encrypted Confidence-Aware Truth Discovery in Mobile Crowdsensing

Zheng, Yifeng; Duan, Huayi; Wang, Cong

doi:10.1109/tifs.2018.2819134

Cited by 133 publications

(44 citation statements)

References 47 publications

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“…A data provider should be issued a higher weight if its collected data are closer to the aggregated results, and in return, its data should be counted more in the process of aggregation. Furthermore, privacy-preserving data quality measurement should also be investigated [98], [99], [100].…”

Section: Data Quality (Dq)mentioning

confidence: 99%

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Xie

Yan

Yao

et al. 2019

IEEE Internet Things J.

152

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Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.

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Section: Data Quality (Dq)mentioning

confidence: 99%

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Xie

Yan

Yao

et al. 2019

IEEE Internet Things J.

152

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“…To handle each floating point number̂︀ , we use a large rounding factor ℒ to scale eacĥ︀ to an integer ℒ •̂︀ whenever needed.̂︀ can be recovered by simply removing ℒ in the final verification process. This is a common trick widely adopted in works such as [21,22,31,32,35,36]. For simplicity, we omit the above operation and default to all̂︀ being integers in the following description.…”

Section: =1mentioning

confidence: 99%

“…As discussed before, our V-PATD excels in performance compared to existing solutions based on Homomorphic Encryption (HE) or SMC. To be more convincing, the latest solutions (based on SMC and HE) [22,35] are also evaluated in our experiments to demonstrate the efficiency of our proposed scheme.…”

Section: Computation Overheadmentioning

confidence: 99%

“…Aggregating the healthcare data submitted from various wearable devices can improve the analysis of the therapeutic effects of new drugs. However, a malicious server may abuse healthcare data because it has full access to individual users' data [30,35]. On the other hand, how to verify the correctness of the aggregated data is another critical issue.…”

Section: Introductionmentioning

confidence: 99%

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Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

et al. 2020

Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

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Truth Discovery (TD) is to infer truthful information by estimating the reliability of users in crowdsensing systems. To protect data privacy, many Privacy-Preserving Truth Discovery (PPTD) approaches have been proposed. However, all existing PPTD solutions do not consider a fundamental issue of trust. That is, if the data aggregator (e.g., the cloud server) is not trustworthy, how can an entity be convinced that the data aggregator has correctly performed the PPTD? A "lazy" cloud server may partially follow the deployed protocols to save its computing and communication resources, or worse, maliciously forge the results for some shady deals. In this paper, we propose V-PATD, the first Verifiable and Privacy-Aware Truth Discovery protocol in crowdsensing systems. In V-PATD, a publicly verifiable approach is designed enabling any entity to verify the correctness of aggregated results returned from the server. Since most of the computation burdens are carried by the cloud server, our verification approach is efficient and scalable. Moreover, users' data is perturbed with the principles of local differential privacy. Security analysis shows that the proposed perturbation mechanism guarantees a high aggregation accuracy even if large noises are added. Compared to existing solutions, extensive experiments conducted on real crowdsensing systems demonstrate the superior performance of V-PATD in terms of accuracy, computation and communication overheads.

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“…Even worse, data submitted by one DP may conflict with that claimed by another one due to the ability variety or malicious behaviors of DPs. Therefore, it is imperative for SP to further process data, such as truth discovery [83]. Besides, some data may contain sensitive information of DPs, it is necessary to process data to reduce the disclosure risk.…”

Section: C) Task Execution and Data Submissionmentioning

confidence: 99%

Privacy protection in mobile crowd sensing: a survey

et al. 2019

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The unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

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Learning the Truth Privately and Confidently: Encrypted Confidence-Aware Truth Discovery in Mobile Crowdsensing

Cited by 133 publications

References 47 publications

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Catch You If You Deceive Me: Verifiable and Privacy-Aware Truth Discovery in Crowdsensing Systems

Privacy protection in mobile crowd sensing: a survey

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