Post-processing of Differentially Private Data: A Fairness Perspective

• URL: http://arxiv.org/abs/2201.09425v1
• Date: Mon, 24 Jan 2022 02:45:03 GMT
• Title: Post-processing of Differentially Private Data: A Fairness Perspective
• Authors: Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
• Abstract summary: This paper shows that post-processing causes disparate impacts on individuals or groups. It analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions. It proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics.
• Score: 53.29035917495491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees. Post-processing is routinely applied in data-release applications, including census data, which are then used to make allocations with substantial societal impacts. This paper shows that post-processing causes disparate impacts on individuals or groups and analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions, such as the allocation of funds informed by US Census data. In the first setting, the paper proposes tight bounds on the unfairness of traditional post-processing mechanisms, giving a unique tool to decision-makers to quantify the disparate impacts introduced by their release. In the second setting, this paper proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics, either reducing fairness issues substantially or reducing the cost of privacy. The theoretical analysis is complemented with numerical simulations on Census data.

Related papers

• Fairness Issues and Mitigations in (Differentially Private) Socio-demographic Data Processes [43.07159967207698]
  This paper shows that surveys of important societal relevance introduce sampling errors that unevenly impact group-level estimates. To address these issues, this paper introduces an optimization approach modeled on real-world survey design processes. Privacy-preserving methods used to determine sampling rates can further impact these fairness issues.
  arXiv  Detail & Related papers  (2024-08-16T01:13:36Z)
• Differentially Private Data Release on Graphs: Inefficiencies and Unfairness [48.96399034594329]
  This paper characterizes the impact of Differential Privacy on bias and unfairness in the context of releasing information about networks. We consider a network release problem where the network structure is known to all, but the weights on edges must be released privately. Our work provides theoretical foundations and empirical evidence into the bias and unfairness arising due to privacy in these networked decision problems.
  arXiv  Detail & Related papers  (2024-08-08T08:37:37Z)
• 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)
• Distribution-Invariant Differential Privacy [4.700764053354502]
  We develop a distribution-invariant privatization (DIP) method to reconcile high statistical accuracy and strict differential privacy. Under the same strictness of privacy protection, DIP achieves superior statistical accuracy in two simulations and on three real-world benchmarks.
  arXiv  Detail & Related papers  (2021-11-08T22:26:50Z)
• Decision Making with Differential Privacy under a Fairness Lens [65.16089054531395]
  The U.S. Census Bureau releases data sets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform to privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private data sets and analyzes their impact on some critical resource allocation tasks under a fairness perspective.
  arXiv  Detail & Related papers  (2021-05-16T21:04:19Z)
• Bias and Variance of Post-processing in Differential Privacy [53.29035917495491]
  Post-processing immunity is a fundamental property of differential privacy. It is often argued that post-processing may introduce bias and increase variance. This paper takes a first step towards understanding the properties of post-processing.
  arXiv  Detail & Related papers  (2020-10-09T02:12:54Z)
• Differential Privacy of Hierarchical Census Data: An Optimization Approach [53.29035917495491]
  Census Bureaus are interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual. Recent events have identified some of the privacy challenges faced by these organizations. This paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
  arXiv  Detail & Related papers  (2020-06-28T18:19:55Z)

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.