A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang, Teng; Zhang, Xuefeng; Feng, Jingyu; Yang, Xinyu

doi:10.3390/s20247030

Cited by 62 publications

(29 citation statements)

References 156 publications

(237 reference statements)

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“…Local differential privacy is conceptually similar to the survey technique known as randomized response [War65]. In fact, the two approaches are mathematically equivalent under many conditions [WZFY20].…”

Section: Local Differential Privacymentioning

confidence: 99%

The Philosophy of Differential Privacy

Bowen¹,

Garfinkel²

2021

Notices Amer. Math. Soc.

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Section: Local Differential Privacymentioning

confidence: 99%

The Philosophy of Differential Privacy

Bowen¹,

Garfinkel²

2021

Notices Amer. Math. Soc.

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“…Surveillance, crowd control, and privacy are three key things for crowd analysis [59]- [66]. The surveillance system should be intelligent and it should protect privacy.…”

Section: Juxtaposition Of Two Surveillance Systemsmentioning

confidence: 99%

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

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Privacy of people is a key factor in surveillance systems. Video camera brings us welloff color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

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“…In local differential privacy method, the differentially private mechanism is applied to the real data on local device instead of sending them to the third-party aggregator. The aggregator receives the data that is already anonymized, and the aggregator will not have any access to real data [42,43]. Local differential privacy method offers a great advantage that the third-party aggregator does not have to be fully trusted.…”

Section: A Differential Privacymentioning

confidence: 99%

“…At the same time, in the -local differential privacy, since the random disturbance is performed by the user himself, depending on the different privacy requirements of the users, different users further achieve the privacy protection by giving different values of the privacy parameter . The light weight implementation methods of LDP can be found in [42].…”

Section: B Local Differential Privacymentioning

confidence: 99%

Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service

Tian

Song

et al. 2021

IEEE Access

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With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things. Consequently, various data collection and incentive mechanisms are investigated for people-centric services. In this paper, we have investigated the problem of privacy-aware people-centric IoT service based on a tailored auction approach. We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices. A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results. Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers' request. The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service. The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers' interests. We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations. The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.

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A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Cited by 62 publications

References 156 publications

The Philosophy of Differential Privacy

The Philosophy of Differential Privacy

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service

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