Deep ${Q}$ -Network-Based Route Scheduling for TNC Vehicles With Passengers’ Location Differential Privacy

Shi, Dian; Ding, Jiahao; Errapotu, Sai Mounika; Hao, Yue; Xu, Wenjun; Zhou, Xiangwei; Pan, Miao

doi:10.1109/jiot.2019.2902815

Cited by 52 publications

(20 citation statements)

References 17 publications

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“…For the privacy-preserving data analysis, the standard privacy metric, Differential privacy (DP) [9,11], is proposed to measure the privacy risk of each data sample in the dataset, and has already been adopted in many machine learning domains [4,8,18,20,24]. Basically, under DP framework, privacy protection is guaranteed by limiting the difference of the distribution of the output regardless of the value change of any one sample in the dataset.…”

Section: Differential Privacymentioning

confidence: 99%

Differentially Private Robust ADMM for Distributed Machine Learning

Ding

Zhang

Chen

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multipliers (ADMM) is a powerful method of designing distributed machine learning algorithm, whereby each agent computes over local datasets and exchanges computation results with its neighbor agents in an iterative procedure. There exists significant privacy leakage during this iterative process if the local data is sensitive. In this paper, we propose a differentially private ADMM algorithm (P-ADMM) to provide dynamic zeroconcentrated differential privacy (dynamic zCDP), by inserting Gaussian noise with linearly decaying variance. We prove that P-ADMM has the same convergence rate compared to the nonprivate counterpart, i.e., O(1/K) with K being the number of iterations and linear convergence for general convex and strongly convex problems while providing differentially private guarantee. Moreover, through our experiments performed on real-world datasets, we empirically show that P-ADMM has the best-known performance among the existing differentially private ADMM based algorithms.• We propose a differentially private ADMM algorithm (P-ADMM) by introducing a Gaussian noise with a linearly decaying variance to address the privacy concerns in distributed machine learning over large datasets. • We introduce a new privacy framework to quantify the privacy leakage in a distributed and iterative setting,

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Section: Differential Privacymentioning

confidence: 99%

Differentially Private Robust ADMM for Distributed Machine Learning

Ding

Zhang

Chen

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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“…Geo-Indistinguishability presents an intuitive way to incorporate location characteristics into differential privacy by calibrating the degree of indistinguishability between locations based on their proximity. Therefore it attracts the interest of researchers generating a rich body of literature ( [24], [25], [26], [27], [28], [29], [26], [27], and [28]). For example, a new randomization algorithm is proposed in [30] based on linear programming techniques to release locations with better utility than the planar Laplacian mechanism.…”

Section: Raw Location Sharingmentioning

confidence: 99%

“…Raw location sharing Distance-based methods [22], [23], [24], [25], [26], [27], [28], [29], [26], [27], [28], and [30] The indistinguishability between any two users' locations is proportional to the distance between these locations. (C1) Utility may be compromised because of the high sensitivity of the distance.…”

Section: Challengesmentioning

confidence: 99%

An Analysis of Differential Privacy Research in Location and Trajectory Data

Errounda

Liu²

2020

Preprint

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Location and trajectory data are routinely collected to generate valuable knowledge about users' pattern behavior. However, releasing location data may jeopardize the privacy of the involved individuals. Differential privacy is a powerful technique that prevents an adversary from inferring the presence or absence of an individual in the original data solely based on the observed data. The first challenge in applying differential privacy in location is that a it usually involves a single user. This shifts the adversary's target to the user's locations instead of presence or absence in the original data. The second challenge is that the inherent correlation between location data, due to people's movement regularity and predictability, gives the adversary an advantage in inferring information about individuals. In this paper, we review the differentially private approaches to tackle these challenges. Our goal is to help newcomers to the field to better understand the state-of-the art by providing a research map that highlights the different challenges in designing differentially private frameworks that tackle the characteristics of location data. We find that in protecting an individual's location privacy, the attention of differential privacy mechanisms shifts to preventing the adversary from inferring the original location based on the observed one. Moreover, we find that the privacy-preserving mechanisms make use of the predictability and regularity of users' movements to design and protect the users' privacy in trajectory data. Finally, we explore how well the presented frameworks succeed in protecting users' locations and trajectories against well-known privacy attacks.

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“…For the privacy-preserving data analysis, the standard privacy metric, Dierential privacy (DP) [9,11], is proposed to measure the privacy risk of each data sample in the dataset, and has already been adopted in many machine learning domains [4,8,18,20,24]. Basically, under DP framework, privacy protection is guaranteed by limiting the dierence of the distribution of the output regardless of the value change of any one sample in the dataset.…”

Section: Dierential Privacymentioning

confidence: 99%

Towards Plausible Differentially Private ADMM Based Distributed Machine Learning

Ding

Wang

Liang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local private data and model exchanges among agents impose critical privacy concerns. Despite some pioneering works to relieve such concerns, dierentially private ADMM still confronts many research challenges. For example, the guarantee of dierential privacy (DP) relies on the premise that the optimality of each local problem can be perfectly attained in each ADMM iteration, which may never happen in practice. The model trained by DP ADMM may have low prediction accuracy. In this paper, we address these concerns by proposing a novel (Improved) Plausible dierentially Private ADMM algorithm, called PP-ADMM and IPP-ADMM. In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee. To further improve the model accuracy and convergence, an improved version IPP-ADMM adopts sparse vector technique (SVT) to determine if an agent should update its neighbors with the current perturbed solution. The agent calculates the dierence of the current solution from that in the last iteration, and if the dierence is larger than a threshold, it passes the solution to neighbors; or otherwise the solution will be discarded. Moreover, we propose to track the total privacy loss under the zero-concentrated DP (zCDP) and provide a generalization performance analysis. Experiments on real-world datasets demonstrate that under the same privacy guarantee, the proposed algorithms are superior to the state of the art in terms of model accuracy and convergence rate. CCS CONCEPTS • Security and privacy ! Privacy-preserving protocols; • Theory of computation ! Massively parallel algorithms; • Computing methodologies ! Supervised learning;

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Deep ${Q}$ -Network-Based Route Scheduling for TNC Vehicles With Passengers’ Location Differential Privacy

Cited by 52 publications

References 17 publications

Differentially Private Robust ADMM for Distributed Machine Learning

Differentially Private Robust ADMM for Distributed Machine Learning

An Analysis of Differential Privacy Research in Location and Trajectory Data

Towards Plausible Differentially Private ADMM Based Distributed Machine Learning

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