P2NIA: Privacy-Preserving Non-Iterative Auditing

• URL: http://arxiv.org/abs/2504.00874v1
• Date: Tue, 01 Apr 2025 15:04:58 GMT
• Title: P2NIA: Privacy-Preserving Non-Iterative Auditing
• Authors: Jade Garcia Bourrée, Hadrien Lautraite, Sébastien Gambs, Gilles Tredan, Erwan Le Merrer, Benoît Rottembourg,
• Abstract summary: The emergence of AI legislation has increased the need to assess the ethical compliance of high-risk AI systems.<n>Traditional auditing methods rely on platforms' application programming interfaces (APIs)<n>We present P2NIA, a novel auditing scheme that proposes a mutually beneficial collaboration for both the auditor and the platform.
• Score: 5.619344845505019
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emergence of AI legislation has increased the need to assess the ethical compliance of high-risk AI systems. Traditional auditing methods rely on platforms' application programming interfaces (APIs), where responses to queries are examined through the lens of fairness requirements. However, such approaches put a significant burden on platforms, as they are forced to maintain APIs while ensuring privacy, facing the possibility of data leaks. This lack of proper collaboration between the two parties, in turn, causes a significant challenge to the auditor, who is subject to estimation bias as they are unaware of the data distribution of the platform. To address these two issues, we present P2NIA, a novel auditing scheme that proposes a mutually beneficial collaboration for both the auditor and the platform. Extensive experiments demonstrate P2NIA's effectiveness in addressing both issues. In summary, our work introduces a privacy-preserving and non-iterative audit scheme that enhances fairness assessments using synthetic or local data, avoiding the challenges associated with traditional API-based audits.

Related papers

• Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [60.881609323604685]
  Large Language Models (LLMs) accessed via black-box APIs introduce a trust challenge. Users pay for services based on advertised model capabilities. providers may covertly substitute the specified model with a cheaper, lower-quality alternative to reduce operational costs. This lack of transparency undermines fairness, erodes trust, and complicates reliable benchmarking.
  arXiv  Detail & Related papers  (2025-04-07T03:57:41Z)
• PII-Bench: Evaluating Query-Aware Privacy Protection Systems [10.52362814808073]
  We propose a query-unrelated PII masking strategy and introduce PII-Bench, the first comprehensive evaluation framework for assessing privacy protection systems.<n>PII-Bench comprises 2,842 test samples across 55 fine-grained PII categories, featuring diverse scenarios from single-subject descriptions to complex multi-party interactions.
  arXiv  Detail & Related papers  (2025-02-25T14:49:08Z)
• Privacy Audit as Bits Transmission: (Im)possibilities for Audit by One Run [7.850976675388593]
  We introduce a unifying framework for privacy audits based on information-theoretic principles. We demystify the method of privacy audit by one run, identifying the conditions under which single-run audits are feasible or infeasible.
  arXiv  Detail & Related papers  (2025-01-29T16:38:51Z)
• From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditing [1.196505602609637]
  Audits can take many forms, including pre-deployment risk assessments, ongoing monitoring, and compliance testing. There are many operational challenges to AI auditing that complicate its implementation. We argue that auditing can be cast as a natural hypothesis test, draw parallels hypothesis testing and legal procedure, and argue that this framing provides clear and interpretable guidance on audit implementation.
  arXiv  Detail & Related papers  (2024-10-07T06:15:46Z)
• An Empirical Study on Compliance with Ranking Transparency in the Software Documentation of EU Online Platforms [7.461555266672227]
  This study empirically evaluate the compliance of six major platforms (Amazon, Bing, Booking, Google, Tripadvisor, and Yahoo) We introduce and test automated compliance assessment tools based on ChatGPT and information retrieval technology. Our findings could help enhance regulatory compliance and align with the United Nations Sustainable Development Goal 10.3.
  arXiv  Detail & Related papers  (2023-12-22T16:08:32Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations [108.89353070722497]
  We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data. We present a new algorithm called Causal-Adjusted Pessimistic (CAP) policy learning, which forms the reward function as the solution of an integral equation system.
  arXiv  Detail & Related papers  (2023-03-20T15:17:31Z)
• Tight Auditing of Differentially Private Machine Learning [77.38590306275877]
  For private machine learning, existing auditing mechanisms are tight. They only give tight estimates under implausible worst-case assumptions. We design an improved auditing scheme that yields tight privacy estimates for natural (not adversarially crafted) datasets.
  arXiv  Detail & Related papers  (2023-02-15T21:40:33Z)
• Debiasing Recommendation by Learning Identifiable Latent Confounders [49.16119112336605]
  Confounding bias arises due to the presence of unmeasured variables that can affect both a user's exposure and feedback. Existing methods either (1) make untenable assumptions about these unmeasured variables or (2) directly infer latent confounders from users' exposure. We propose a novel method, i.e., identifiable deconfounder (iDCF), which leverages a set of proxy variables to resolve the aforementioned non-identification issue.
  arXiv  Detail & Related papers  (2023-02-10T05:10:26Z)
• Having your Privacy Cake and Eating it Too: Platform-supported Auditing of Social Media Algorithms for Public Interest [70.02478301291264]
  Social media platforms curate access to information and opportunities, and so play a critical role in shaping public discourse. Prior studies have used black-box methods to show that these algorithms can lead to biased or discriminatory outcomes. We propose a new method for platform-supported auditing that can meet the goals of the proposed legislation.
  arXiv  Detail & Related papers  (2022-07-18T17:32:35Z)

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.