Mobile Crowdsensing Task Allocation optimization with Differentially Private Location Privacy

User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.

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Data Quality Aware Task Allocation With Budget Constraint in Mobile Crowdsensing

IEEE Access ◽

10.1109/access.2018.2865095 ◽

2018 ◽

Vol 6 ◽

pp. 48010-48020 ◽

Cited By ~ 4

Author(s):

Xiaohui Wei ◽

Yongfang Wang ◽

Jingweijia Tan ◽

Shang Gao

Keyword(s):

Data Quality ◽

Task Allocation ◽

Budget Constraint ◽

Mobile Crowdsensing

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A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

10.21203/rs.3.rs-552689/v1 ◽

2021 ◽

Author(s):

Xiaodong Zheng ◽

Qi Yuan ◽

Bo Wang ◽

Lei Zhang

Keyword(s):

Experimental Verification ◽

Task Allocation ◽

Privacy Preservation ◽

Location Privacy ◽

Homomorphic Encryption ◽

Security Analysis ◽

Whole Process ◽

Privacy Violation

Abstract In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

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