Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios

• URL: http://arxiv.org/abs/2509.07505v1
• Date: Tue, 09 Sep 2025 08:38:52 GMT
• Title: Extension of Spatial k-Anonymity: New Metrics for Assessing the Anonymity of Geomasked Data Considering Realistic Attack Scenarios
• Authors: Simon Cremer, Lydia Jehmlich, Rainer Lenz,
• Abstract summary: The degree of anonymity of anonymized georeferenced datasets is often measured by the so-called metric of spatial k-anonymity.<n>This article classifies the potential data attack scenarios in the context of anonymized georeferenced microdata and introduces appropriate metrics that enable a comprehensive assessment of anonymity adapted to potential data attack scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spatial data are gaining increasing importance in many areas of research. Particularly spatial health data are becoming increasingly important for medical research, for example, to better understand relationships between environmental factors and disease patterns. However, their use is often restricted by legal data protection regulations, since georeferenced personal information carries a high risk of re-identification of individuals. To address this issue, what are called geomasking methods are applied to guarantee data protection through targeted displacement of individual data points, while simultaneously maintaining analytical validity within a tolerable range. In the current literature the degree of anonymity of such anonymized georeferenced datasets is often measured by the so-called metric of spatial k-anonymity. However, this metric has considerable shortcomings, particularly regarding its resilience against realistic data attack scenarios. This article classifies the potential data attack scenarios in the context of anonymized georeferenced microdata and introduces appropriate metrics that enable a comprehensive assessment of anonymity adapted to potential data attack scenarios.

Related papers

• Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective [18.404146545866812]
  Training generative machine learning models to produce synthetic data has become a popular approach for enhancing privacy in data sharing.<n>As this typically involves processing sensitive personal information, releasing either the trained model or generated synthetic anonymity can still pose privacy risks.<n>We argue that meaningful assessments must account for the capabilities and properties of underlying generative model and be grounded in state-of-the-art privacy attacks.
  arXiv  Detail & Related papers  (2026-01-30T00:57:41Z)
• A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage [77.83757117924995]
  We propose a new framework that evaluates re-identification attacks to quantify individual privacy risks upon data release.<n>Our approach shows that seemingly innocuous auxiliary information can be used to infer sensitive attributes like age or substance use history from sanitized data.
  arXiv  Detail & Related papers  (2025-04-28T01:16:27Z)
• Investigating Vulnerabilities of GPS Trip Data to Trajectory-User Linking Attacks [49.1574468325115]
  We propose a novel attack to reconstruct user identifiers in GPS trip datasets consisting of single trips.<n>We show that the risk of re-identification is significant even when personal identifiers have been removed.<n>Further investigations indicate that users who frequently visit locations that are only visited by a small number of others tend to be more vulnerable to re-identification.
  arXiv  Detail & Related papers  (2025-02-12T08:54:49Z)
• Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
  Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs. This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-15T08:09:28Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Privacy risk in GeoData: A survey [3.7228963206288967]
  We analyse different geomasking techniques proposed to protect individuals' privacy in geodata. We propose a taxonomy to characterise these techniques across various dimensions. Our proposed taxonomy serves as a practical resource for data custodians, offering them a means to navigate the extensive array of existing privacy mechanisms.
  arXiv  Detail & Related papers  (2024-02-06T00:55:06Z)
• Where you go is who you are -- A study on machine learning based semantic privacy attacks [3.259843027596329]
  We present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data.
  arXiv  Detail & Related papers  (2023-10-26T17:56:50Z)
• A Trajectory K-Anonymity Model Based on Point Density and Partition [0.0]
  This paper develops a trajectory K-anonymity model based on Point Density and Partition (K PDP) It successfully resists re-identification attacks and reduces the data utility loss of the k-anonymized dataset.
  arXiv  Detail & Related papers  (2023-07-31T17:10:56Z)
• Membership Inference Attacks against Synthetic Data through Overfitting Detection [84.02632160692995]
  We argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution. We propose DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
  arXiv  Detail & Related papers  (2023-02-24T11:27:39Z)
• Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
  We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
  arXiv  Detail & Related papers  (2022-05-24T19:37:11Z)
• Hide-and-Seek Privacy Challenge [88.49671206936259]
  The NeurIPS 2020 Hide-and-Seek Privacy Challenge is a novel two-tracked competition to accelerate progress in tackling both problems. In our head-to-head format, participants in the synthetic data generation track (i.e. "hiders") and the patient re-identification track (i.e. "seekers") are directly pitted against each other by way of a new, high-quality intensive care time-series dataset.
  arXiv  Detail & Related papers  (2020-07-23T15:50:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.