A Dynamic Privacy Protection Mechanism for Spatiotemporal Crowdsourcing

Liu, Tianen; Wang, Yingjie; Cai, Zhipeng; Tong, Xiangrong; Zhao, Jindong

doi:10.1155/2020/8892954

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Therefore, it is crucial the protection of spatio-temporal information. To avoid background knowledge and homogeneity attacks, the time attribute may be added in the de-identification of the location attribute [101]. In continuous data release, outliers are also a problem.…”

Section: Dynamic Datamentioning

confidence: 99%

Survey on Privacy-Preserving Techniques for Data Publishing

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

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The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques. However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individuals' privacy while maintaining the interpretability of the data, i.e. its usefulness. Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing privacy-preserving techniques used in microdata de-identification, privacy measures suitable for several disclosure types and, information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review taxonomies of privacy-preserving techniques, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.

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Section: Dynamic Datamentioning

confidence: 99%

Survey on Privacy-Preserving Techniques for Data Publishing

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…But the encryption protocol application process is too complicated. 31 More importantly, they are not suitable for mobile devices. But the differential privacy disturbance mechanisms are adapted to these situations.…”

Section: Related Workmentioning

confidence: 99%

“…Zhou et al 30 used homomorphic encryption technology which belongs the symmetric encryption technology to ensure the security of data transmission. But the encryption protocol application process is too complicated 31 . More importantly, they are not suitable for mobile devices.…”

Section: Related Workmentioning

confidence: 99%

A two‐stage privacy protection mechanism based on blockchain in mobile crowdsourcing

et al. 2021

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With the rise of the Internet of Things (IoT) and fifth‐generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third‐party platform. However, since the credibility of the third‐party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two‐stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third‐party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.

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