An Architecture for Privacy-Preserving Telemetry Scheme

• URL: http://arxiv.org/abs/2507.06350v1
• Date: Tue, 08 Jul 2025 19:20:56 GMT
• Title: An Architecture for Privacy-Preserving Telemetry Scheme
• Authors: Kenneth Odoh,
• Abstract summary: We present a privacy-preserving telemetry aggregation scheme.<n>The design philosophy follows a client-server architecture.<n>We provide an implementation that focuses on frequency estimation with a histogram of a known dictionary.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a privacy-preserving telemetry aggregation scheme. Our underlying frequency estimation routine works within the framework of differential privacy. The design philosophy follows a client-server architecture. Furthermore, the system uses a local differential privacy scheme where data gets randomized on the client before submitting the request to the resource server. This scheme allows for data analysis on de-identified data by carefully adding noise to prevent re-identification attacks, thereby facilitating public data release without compromising the identifiability of the individual record. This work further enhances privacy guarantees by leveraging Oblivious HTTP (OHTTP) to achieve increased privacy protection for data in transit that addresses pre-existing privacy vulnerabilities in raw HTTP. We provide an implementation that focuses on frequency estimation with a histogram of a known dictionary. Our resulting formulation based on OHTTP has provided stricter privacy safeguards when compared to trusting an organization to manually delete identifying information from the client's request in the ingestor as deployed in reference work~\cite{apple2017}. Code available at https://github.com/kenluck2001/miscellaneous/tree/master/src/Privacy-Preserving-Telemetry.

Related papers

• Information-theoretic Estimation of the Risk of Privacy Leaks [0.0]
  dependencies between items in a dataset can lead to privacy leaks.<n>We measure the correlation between the original data and their noisy responses from a randomizer as an indicator of potential privacy breaches.
  arXiv  Detail & Related papers  (2025-06-14T03:39:11Z)
• PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration [20.05248442344211]
  PrivacyRestore is a plug-and-play method to protect the privacy of user inputs during inference.<n>We create three datasets, covering medical and legal domains, to evaluate the effectiveness of PrivacyRestore.
  arXiv  Detail & Related papers  (2024-06-03T14:57:39Z)
• RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs [2.1765174838950494]
  We study the new paradigm for collecting and protecting the data produced by ever-increasing sensor devices. Most previous studies on co-design of data aggregation and privacy preservation assume that a trusted fusion center adheres to privacy regimes. We propose a novel paradigm (called RASE), which can be generalized into a 3-step sequential procedure, noise addition, followed by random permutation, and then parameter estimation.
  arXiv  Detail & Related papers  (2024-05-31T15:21:38Z)
• Secure Aggregation is Not Private Against Membership Inference Attacks [66.59892736942953]
  We investigate the privacy implications of SecAgg in federated learning. We show that SecAgg offers weak privacy against membership inference attacks even in a single training round. Our findings underscore the imperative for additional privacy-enhancing mechanisms, such as noise injection.
  arXiv  Detail & Related papers  (2024-03-26T15:07:58Z)
• A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
  We propose a new differential privacy paradigm called estimate-verify-release (EVR) EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output. Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
  arXiv  Detail & Related papers  (2023-04-17T00:38:01Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• Algorithms with More Granular Differential Privacy Guarantees [65.3684804101664]
  We consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this work, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person.
  arXiv  Detail & Related papers  (2022-09-08T22:43:50Z)
• Smooth Anonymity for Sparse Graphs [69.1048938123063]
  differential privacy has emerged as the gold standard of privacy, however, when it comes to sharing sparse datasets. In this work, we consider a variation of $k$-anonymity, which we call smooth-$k$-anonymity, and design simple large-scale algorithms that efficiently provide smooth-$k$-anonymity.
  arXiv  Detail & Related papers  (2022-07-13T17:09:25Z)
• A Shuffling Framework for Local Differential Privacy [40.92785300658643]
  ldp deployments are vulnerable to inference attacks as an adversary can link the noisy responses to their identity. An alternative model, shuffle DP, prevents this by shuffling the noisy responses uniformly at random. We show that systematic shuffling of the noisy responses can thwart specific inference attacks while retaining some meaningful data learnability.
  arXiv  Detail & Related papers  (2021-06-11T20:36:23Z)
• Location Trace Privacy Under Conditional Priors [22.970796265042246]
  We propose a R'enyi divergence based privacy framework for bounding expected privacy loss for conditionally dependent data. We demonstrate an algorithm for achieving this privacy under conditional priors.
  arXiv  Detail & Related papers  (2021-02-23T21:55:34Z)
• PGLP: Customizable and Rigorous Location Privacy through Policy Graph [68.3736286350014]
  We propose a new location privacy notion called PGLP, which provides a rich interface to release private locations with customizable and rigorous privacy guarantee. Specifically, we formalize a user's location privacy requirements using a textitlocation policy graph, which is expressive and customizable. Third, we design a private location trace release framework that pipelines the detection of location exposure, policy graph repair, and private trajectory release with customizable and rigorous location privacy.
  arXiv  Detail & Related papers  (2020-05-04T04:25:59Z)

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.