Measuring privacy in high dimensional microdata collections

Boukoros, Spyros; Katzenbeisser, Stefan

doi:10.1145/3098954.3098977

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The monetary incentives are the most intuitive making sensory data a commodity in the free market 10 . Nevertheless, users' concern regarding privacy disclosure in MCS is a key problem to inhibit user adoption 11,12 . Some personal information such as the user's location, sound, or images may be included in sensory data collected by users using smart mobile devices, and the sensory data are directly uploaded to the crowdsensor's server or the TP.…”

Section: Introductionmentioning

confidence: 99%

A blockchain‐based mobile crowdsensing scheme with enhanced privacy

Peng

Guan

Liu

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

With the popularity and development of sensors‐containing intelligent terminals, mobile crowdsensing system (MCS) based on the Internet of Things (IoT) has become a new paradigm of application. By the MCS, the pervasive smart device users are enabled to collect large‐scale data cost‐effectively, for crowd intelligent extraction and human‐centric service delivery. However, most of the existing MCSs are based on a centralized structure vulnerable to attacks and intrusions. Moreover, the data collected through crowdsensing are diverse and difficult to guarantee user privacy, especially during the payment and data upload stages. In this article, we propose a blockchain‐based privacy‐preserving crowdsensing (BPPC) scheme based on the distributed structure, to protect user privacy. First, we combine the multiblockchain technology and K‐anonymity to construct anonymity groups for the confusion. Second, we present the random algorithm HashProof to select candidates from the anonymity groups to avoid deployment of Trusted Third Party (TTP) or agent server. Ultimately, we design encryption‐based algorithms building trust and authentication mechanisms in the system to guarantee the confidentiality of user data and achieve the accurate distribution of rewards. To verify the effectiveness and efficiency of the BPPC scheme, extensive experiments were conducted.

show abstract

Section: Introductionmentioning

confidence: 99%