Differential Private Spatial Decomposition and Location Publishing Based on Unbalanced Quadtree Partition Algorithm

Yan, Yan; Gao, Xin; Mahmood, Adnan; Tao, Feng; Xie, Peng

doi:10.1109/access.2020.2999580

Cited by 20 publications

(17 citation statements)

References 24 publications

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“…For any data sets and that differ by one record, a randomized algorithm provides DP protection with ℰ value (ℰ-DP) if and only if the following is satisfied [13]- [14]:…”

Section: B the Basic Concepts Of Differential Privacymentioning

confidence: 99%

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Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim

2020

IEEE Access

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Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently. FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users. Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage. To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation. To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations. Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators. Therefore, we can take various benefits in a rational way under the dynamic changing FL environments. Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.

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“…For any data sets and that differ by one record, a randomized algorithm provides DP protection with ℰ value (ℰ-DP) if and only if the following is satisfied [13]- [14]:…”

Section: B the Basic Concepts Of Differential Privacymentioning

confidence: 99%

“…Therefore, if the attacker knows most of the records in the original data set, he still cannot accurately determine whether a record is in and . The smaller ℰ value, the lower is the probability of distinguishing the two datasets and the higher the degree of privacy protection [13]- [14].…”

Section: B the Basic Concepts Of Differential Privacymentioning

confidence: 99%

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim

2020

IEEE Access

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“…A geometric budget allocation strategy increases the budget geometrically at each tree level so the leaf counts have the highest accuracy. To improve the query's utility, it is also possible to use an unbalanced quadtree partition algorithm based on regional uniformity as adopted in [49]. Furthermore, the privacy budget allocation scheme is adjusted to ensure the effectiveness of the differential privacy model.…”

Section: Location Aggregate Sharingmentioning

confidence: 99%

“…Private Spatial Decomposition [48] and [49] Recursively divide the location using a tree structure, then induce noise to the count at each node of the tree…”

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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“…Google has recently published a Google COVID-19 Community Mobility Reports 17 to help public health authorities understand the mobility trends over time across different categories of places, such as retail, recreation, groceries etc., in response to imposed policies aimed at combating COVID-19 pandemic. The authors in [115] use differential privacy to publish statistical information of two-dimensional location data to ensure location privacy. Other works, such as [116], [117], partition data dimensions to minimise the amount of noise, and in order to achieve higher privacy accuracy, by using differential privacy in response to the given set of queries.…”

Section: Differential Privacymentioning

confidence: 99%

Privacy in targeted advertising: A survey

Ullah¹,

Boreli²,

Kanhere³

2020

Preprint

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Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.<br>

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Differential Private Spatial Decomposition and Location Publishing Based on Unbalanced Quadtree Partition Algorithm

Cited by 20 publications

References 24 publications

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

An Analysis of Differential Privacy Research in Location and Trajectory Data

Privacy in targeted advertising: A survey

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