The Accuracy-Privacy Trade-off of Mobile Crowdsensing

Abstract

Mobile crowdsensing has emerged as an efficient sensing paradigm which combines the crowd intelligence and the sensing power of mobile devices, e.g., mobile phones and Internet of Things (IoT) gadgets. This article addresses the contradicting incentives of privacy preservation by crowdsensing users and accuracy maximization and collection of true data by service providers. We firstly define the individual contributions of crowdsensing users based on the accuracy in data analytics achieved by the service provider from buying their data. We then propose a truthful mechanism for achieving high service accuracy while protecting the privacy based on the user preferences. The users are incentivized to provide true data by being paid based on their individual contribution to the overall service accuracy. Moreover, we propose a coalition strategy which allows users to cooperate in providing their data under one identity, increasing their anonymity privacy protection, and sharing the resulting payoff. Finally, we outline important open research directions in mobile and people-centric crowdsensing.

Secure mobile crowdsensing based on deep learning

“…DL-BASED PRIVACY PROTECTION Privacy protection for sensing report transmission and storage at cloud servers is essential for practical MCS systems. Data anonymization methods, such as k-anonymity, l-diversity and t-closeness [2], provide anonymous quasi-identifiers and protect sensitive information in database for MCS. However, the sensing reports containing high-dimensional data streams such as images and videos, cannot be satisfied with such traditional data anonymization schemes, due to the difficulty defining the quasi-identifiers and sensitive attributes.…”

“…The DNN-base privacy protection method as proposed in [8] applies different levels of Gaussian noise in the training parameters to provide differential privacy which reduces the sensing accuracy as well. To balance the privacy preservation and the sensing accuracy, a coalition MCS incentive mechanism as designed in [2] evaluates the user payoff and compares with a threshold that currently is simply set to be zero. However, such a test threshold is not always optimal and sometimes even loses the balance between the privacy protection and sensing accuracy in dynamic MCS networks.…”

“…Participants recruited by an MCS platform monitor the surrounding features and upload the sensing reports to MCS servers that in turn extract interest information from the sensing reports. Security and privacy are critical for MCS systems, as mobile devices are controlled by selfish and autonomous users who can launch insider attacks such as faked sensing attacks and have privacy concerns, and the transmission over radio networks such as 3G/4G, WiFi and vehicular ad-hoc networks (VANETs) are vulnerable to security threats such as jamming, distributed denial of service attacks (DoS), spoofing attacks, Sybil attacks, faked sensing attacks and smart attacks [2].…”

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

“…Mechanism. The data = {( , )} =1 perceived by the sensing user in the th perceptual task is a tuples containing the data feature set ∈ R and the class tag ∈ R, where is the number of data tuples and is the number of data attributes [31]. Feature set consists of sensing data, such as GPS data in smart trip services and personalized recommended social network behavior data.…”

A new differential privacy preserving crowdsensing scheme based on the Owen value

“…Performance analysis: we evaluate the performance of the proposed scheme based on the simulation model. Numerical study demonstrates that the overall system performance of our proposed scheme can be significantly improved by comparing to the existing [17][18][19] schemes.…”