Quantifying Location Privacy Risks Under Heterogeneous Correlations

Li, Bing; Zhu, Hong; Xie, Meiyi

doi:10.1109/access.2021.3056152

Cited by 9 publications

(9 citation statements)

References 46 publications

(102 reference statements)

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“…The future technologies should focus more on the geospatial data masking to effectively preserve people's privacy. The important research direction is to preserve people's privacy in heterogeneous sources data fusion [106]. Since, fusing multiple and heterogeneous sources data can assist in impacting group privacy along with an individual's privacy which has a range of negative consequences.…”

Section: Brief Insights On Data Management In the Epidemic Control Mementioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

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Section: Brief Insights On Data Management In the Epidemic Control Mementioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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“…Location privacy protection algorithms based on location perturba-tion can be applied to the scenarios where individual locations are continuously released, e.g., online location-based services. However, locations in the trajectory are perturbed independently and the spatial-temporal correlations within the whole trajectory are neglected, so privacy could be compromised by adversaries who have access to the correlations [23].…”

Section: Location Perturbationmentioning

confidence: 99%

“…However, the authors focus on the data utility of a single location while neglecting that of whole the trajectory. In addition, independently protecting each actual location without a comprehensive consideration of the correlations in the trajectory was subject to location inference attacks and de-anonymization attacks [23]. Huang et al [4] discover a specific applying scenario of location privacy protection, ridesharing.…”

Section: Location Perturbationmentioning

confidence: 99%

“…CRF is a discriminative undirected graphical model that supports auxiliary dependency within a sequence and performs well over many problems, such as sequence inference [31]. Recent works have shown that CRF can capture individual movement patterns from spatial, temporal, and semantic aspects, even though the trajectory data are sparse [23]. In particular, CRF is good at learning the transition patterns between locations, bringing about good preservation of data utility, such as periodic movement patterns.…”

Section: Constructing Obfuscated Trajectory Synthesizermentioning

confidence: 99%

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Releasing Differentially Private Trajectories with Optimized Data Utility

Zhu

Xie

2022

Applied Sciences

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The ubiquity of GPS-enabled devices has resulted in an abundance of data about individual trajectories. Releasing trajectories enables a range of data analysis tasks, such as urban planning, but it also poses a risk in compromising individual location privacy. To tackle this issue, a number of location privacy protection algorithms are proposed. However, existing works are primarily concerned with maintaining the trajectory data geographic utility and neglect the semantic utility. Thus, many data analysis tasks relying on utility, e.g., semantic annotation, suffer from poor performance. Furthermore, the released trajectories are vulnerable to location inference attacks and de-anonymization attacks due to insufficient privacy guarantee. In this paper, to design a location privacy protection algorithm for releasing an offline trajectory dataset to potentially untrusted analyzers, we propose a utility-optimized and differentially private trajectory synthesizer (UDPT) with two novel features. First, UDPT simultaneously preserves both geographical utility and semantic utility by solving a data utility optimization problem with a genetic algorithm. Second, UDPT provides a formal and provable guarantee against privacy attacks by synthesizing obfuscated trajectories in a differentially private manner. Extensive experimental evaluations on real-world datasets demonstrate UDPT’s outperformance against state-of-the-art works in terms of data utility and privacy.

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“…Generally, privacy risks are highly associated with the nature of the data, the data owners, the settings for data processing (centralized, decentralized, hybrid), adversaries' skills, etc. Quantifying privacy risks accurately needs domain expertise and underlying data knowledge in a finegrained manner [408]. Thus, devising accurate privacy-risk quantification methods for different data styles/sectors is an important avenue for future research.…”

Section: B Promising Future Research Directionsmentioning

confidence: 99%

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

Majeed

Hwang

2021

IEEE Access

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Since the emergence of coronavirus disease-2019 (COVID-19) outbreak, every country has implemented digital solutions in the form of mobile applications, web-based frameworks, and/or integrated platforms in which huge amounts of personal data are collected for various purposes (e.g., contact tracing, suspect search, and quarantine monitoring). These systems not only collect basic data about individuals but, in most cases, very sensitive data like their movements, spatio-temporal activities, travel history, visits to churches/clubs, purchases, and social interactions. While collection and utilization of person-specific data in different contexts is essential to limiting the spread of COVID-19, it increases the chances of privacy breaches and personal data misuse. Recently, many privacy protection techniques (PPTs) have been proposed based on the person-specific data included in different data types (e.g., tables, graphs, matrixes, barcodes, and geospatial data), and epidemic containment strategies (ECSs) (contact tracing, quarantine monitoring, symptom reports, etc.) in order to minimize privacy breaches and to permit only the intended uses of such personal data. In this paper, we present an extensive review of the PPTs that have been recently proposed to address the diverse privacy requirements/concerns stemming from the COVID-19 pandemic. We describe the heterogeneous types of data collected to control this pandemic, and the corresponding PPTs, as well as the paradigm shifts in personal data handling brought on by this pandemic. We systemically map the recently proposed PPTs into various ECSs and data lifecycle phases, and present an in-depth review of existing PPTs and evaluation metrics employed for analysis of their suitability. We describe various PPTs developed during the COVID-19 period that leverage emerging technologies, such as federated learning, blockchain, privacy by design, and swarm learning, to name a few. Furthermore, we discuss the challenges of preserving individual privacy during a pandemic, the role of privacy regulations/laws, and promising future research directions. With this article, our aim is to highlight the recent PPTs that have been specifically proposed for the COVID-19 arena, and point out research gaps for future developments in this regard.

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Quantifying Location Privacy Risks Under Heterogeneous Correlations

Cited by 9 publications

References 46 publications

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Releasing Differentially Private Trajectories with Optimized Data Utility

A Comprehensive Analysis of Privacy Protection Techniques Developed for COVID-19 Pandemic

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