Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping

Lin, Yue

doi:10.1080/15230406.2023.2267967

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…In addition to spatial k-anonymity, variants of differential privacy have been developed to ensure the anonymity of individual data subjects in geospatial data. One practical variant is geo-indistinguishability, which aims to add noise for protecting location-based services and data products (Andrés et al, 2013;Lin, 2023a). The US Census Bureau has also devised a mechanism based on differential privacy called the TopDown Algorithm for the privacy protection of geographically aggregated census tables (Abowd, 2018;Abowd et al, 2022).…”

Section: Location Privacy and Anonymitymentioning

confidence: 99%

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

Lin

2024

Environment and Planning F

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Privacy has been an important topic within the geospatial science community, particularly driven by the widespread adoption of geospatial technologies such as mobile devices and the vast amount of location data they generate. This has sparked considerable interest in location privacy that is specifically dedicated to the protection of location information. However, existing literature on location privacy mostly focuses on preserving anonymity and protecting against individual identifiability when using geospatial data. While this is undoubtedly valuable, it may prove insufficient in a landscape characterized by pervasive data collection and analytics. This article argues that the powerful capabilities of algorithms in data-intensive geospatial analytics allow for the profiling of groups of individuals and the prediction of sensitive information that has not been explicitly collected, without necessarily compromising individual identifiability. Nonetheless, these practices pose severe threats to privacy and can contribute to inequality in the treatment of certain populations, leading to structural and societal challenges. In response to these challenges, a collective approach should be embraced to address location privacy concerns. Both regulatory and technical practices need to acknowledge the interdependency of privacy, while individuals should cultivate an awareness of cooperation in privacy protection.

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Section: Location Privacy and Anonymitymentioning

confidence: 99%

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

Lin

2024

Environment and Planning F

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“…The framework requires the input of an individual-level data set that covers the entire population of a region, but such data is typically not publicly available due to individual privacy protection. A synthetic individual-level data set is therefore used for our computational experiments (Lin andXiao 2022, 2023c). Specifically, this data set contains 1,163,414 individuals in 284 census tracts of Franklin County, Ohio, and each individual has three attributes: voting age (V), ethnicity (E), and race (R).…”

Section: Computational Experimentsmentioning

confidence: 99%

“…For example, Wieland et al (2008) propose an optimization method to relocate sensitive disease‐related points to protect patients' privacy while minimizing the expected distance displaced required to achieve a certain privacy level. In other research, some incorporate strategies into method design to eliminate the displacement distance for point data to maintain utility (Seidl, Jankowski, and Tsou 2016; Zurbarán et al 2018; Houfaf‐Khoufaf, Touya, and Le Guilcher 2021; Lin 2023). Additionally, there are studies that assess the utility of privacy‐preserved data as an indicator of the effectiveness of privacy‐preserving methods (Kounadi and Leitner 2016; Wang, Kim, and Kwan 2022).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Tradeoff Between Privacy and Utility of Complete‐count Census Data Using a Multiobjective Optimization Approach

Lin,

Xiao

2024

Geographical Analysis

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Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data release. Therefore, it is necessary to investigate the tradeoff between privacy and utility before making a final decision on the level of privacy protection. In this article, we propose a multiobjective optimization framework to generate multiple optimal solutions that satisfy the two objectives of privacy and utility, as well as to analyze the tradeoff between privacy and utility for decision‐making. This framework relocates individuals susceptible to revealing their identities to protect their privacy. We maximize the number of individuals relocated while maximizing the utility of the data after relocations. The proposed framework is tested using synthetic population data in Franklin County, Ohio. Our experimental results show that the framework can efficiently generate a collection of optimal solutions and can be used to effectively balance privacy and utility.

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GeoSecure-B:A Method for Secure Bearing Calculation

Patil,

Rajan,

Atrey

2024

2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR)

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Geo-indistinguishable masking: enhancing privacy protection in spatial point mapping

Cited by 3 publications

References 53 publications

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

Moving beyond anonymity: Embracing a collective approach to location privacy in data-intensive geospatial analytics

Exploring the Tradeoff Between Privacy and Utility of Complete‐count Census Data Using a Multiobjective Optimization Approach

GeoSecure-B:A Method for Secure Bearing Calculation

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