Bistochastically private release of longitudinal data

• URL: http://arxiv.org/abs/2508.10606v1
• Date: Thu, 14 Aug 2025 12:48:06 GMT
• Title: Bistochastically private release of longitudinal data
• Abstract summary: longitudinal data, i.e. individuals observed over multiple periods, are increasingly collected.<n>Few extensions, essentially k-anonymity based, of popular privacy tools have been proposed to deal with the challenges posed by longitudinal data.<n>This paper proposes a simple approach for their anonymization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although the bulk of the research in privacy and statistical disclosure control is designed for cross-sectional data, i.e. data where individuals are observed at one single point in time, longitudinal data, i.e. individuals observed over multiple periods, are increasingly collected. Such data enhance undoubtedly the possibility of statistical analysis compared to cross-sectional data, but also come with one additional layer of information, individual trajectories, that must remain practically useful in a privacy-preserving way. Few extensions, essentially k-anonymity based, of popular privacy tools have been proposed to deal with the challenges posed by longitudinal data, and these proposals are often complex. By considering randomized response, and specifically its recent bistochastic extension, in the context of longitudinal data, this paper proposes a simple approach for their anonymization. After having characterized new results on bistochastic matrices, we show that a simple relationship exists between the protection of each data set released at each period, and the protection of individuals trajectories over time. In turn, this relationship can be tuned according to desired protection and information requirements. We illustrate the application of the proposed approach by an empirical example.

Related papers

• Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases [6.8930591070765495]
  We investigate whether an attacker can compromise privacy in sequential data releases by exploiting dependencies between consecutive publications.<n>We propose a novel attack model that captures these sequential dependencies by integrating a Hidden Markov Model with a reinforcement learning-based bi-directional inference mechanism.<n>Our results reveal a fundamental privacy risk inherent to sequential data publishing, where individually protected releases can collectively leak sensitive information when analyzed temporally.
  arXiv  Detail & Related papers  (2025-10-28T04:32:42Z)
• Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation [9.819636361032256]
  Differentially Private Synthetic Data Generation is a key enabler of private and secure data sharing.<n>Recent literature has explored scenarios where a small amount of public data is used to help enhance the quality of synthetic data.<n>We propose a novel framework that adapts horizontal public-assisted methods into the vertical setting.
  arXiv  Detail & Related papers  (2025-04-15T08:59:03Z)
• 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)
• Bistochastically private release of data streams with zero delay [0.0]
  We propose a protocol for the anonymization of data streams that achieves zero delay while exhibiting formal privacy guarantees. We show that it is feasible to achieve an atomic processing of individuals entering a stream, in-stead of proceeding by batches.
  arXiv  Detail & Related papers  (2024-02-25T14:18:14Z)
• Federated Experiment Design under Distributed Differential Privacy [31.06808163362162]
  We focus on the rigorous protection of users' privacy while minimizing the trust toward service providers. Although a vital component in modern A/B testing, private distributed experimentation has not previously been studied. We show how these mechanisms can be scaled up to handle the very large number of participants commonly found in practice.
  arXiv  Detail & Related papers  (2023-11-07T22:38:56Z)
• Simulation-based Bayesian Inference from Privacy Protected Data [0.0]
  We propose simulation-based inference methods from privacy-protected datasets.<n>We illustrate our methods on discrete time-series data under an infectious disease model and with ordinary linear regression models.
  arXiv  Detail & Related papers  (2023-10-19T14:34:17Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections [19.148874215745135]
  We study the problem of continually releasing differentially private synthetic data from longitudinal data collections. We introduce a model where, in every time step, each individual reports a new data element. We give continual synthetic data generation algorithms that preserve two basic types of queries.
  arXiv  Detail & Related papers  (2023-06-13T16:22:08Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization [58.155151571362914]
  We propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases. splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable privacy budget. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy.
  arXiv  Detail & Related papers  (2022-08-24T17:52:43Z)
• 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.