We propose a novel Bayesian approach for the adaptive and online estimation
of the frequency distribution of a finite number of categories under the local
differential privacy (LDP) framework. The proposed algorithm performs Bayesian
parameter estimation via posterior sampling and adapts the randomization
mechanism for LDP based on the obtained posterior samples. We propose a
randomized mechanism for LDP which uses a subset of categories as an input and
whose performance depends on the selected subset and the true frequency
distribution. By using the posterior sample as an estimate of the frequency
distribution, the algorithm performs a computationally tractable subset
selection step to maximize the utility of the privatized response of the next
user. We propose several utility functions related to well-known information
metrics, such as (but not limited to) Fisher information matrix, total
variation distance, and information entropy. We compare each of these utility
metrics in terms of their computational complexity. We employ stochastic
gradient Langevin dynamics for posterior sampling, a computationally efficient
approximate Markov chain Monte Carlo method. We provide a theoretical analysis
showing that (i) the posterior distribution targeted by the algorithm converges
to the true parameter even for approximate posterior sampling, and (ii) the
algorithm selects the optimal subset with high probability if posterior
sampling is performed exactly. We also provide numerical results that
empirically demonstrate the estimation accuracy of our algorithm where we
compare it with nonadaptive and semi-adaptive approaches under experimental
settings with various combinations of privacy parameters and population
distribution parameters.

我们提出了一种新颖的贝叶斯方法，用于局部差分隐私 (LDP) 框架下有限类别频率分布的自适应在线估计，并通过后验抽样进行贝叶斯参数估计，使基于获得的后验样本调整 LDP 的随机化机制。我们提出了一种基于随机化机制的 LDP，它使用类别子集作为输入，并且其性能取决于所选择的子集和真实频率分布。通过使用后验样本作为频率分布的估计，算法执行可计算子集选择步骤，以最大化下一个用户的隐私响应效用。我们提出了几个与著名信息度量相关的效用函数，如 Fisher 信息矩阵、总变差距离和信息熵。我们比较了每个效用度量在计算复杂度方面的差异。我们采用随机梯度 Langevin 动力学进行后验抽样，这是一种计算高效的近似马尔可夫链蒙特卡洛方法。我们提供了理论分析，证明了算法所针对的后验分布收敛于真实参数，即使进行了近似后验抽样；如果进行了精确的后验抽样，算法将具有高概率选择出最佳子集。我们还提供了数值结果，通过在不同隐私参数和人群分布参数的实验设置下与非自适应和半自适应方法进行比较，经验证明了我们算法的估计精确性。

自适应在线贝叶斯频率分布估计与局部差分隐私

Adaptive Online Bayesian Estimation of Frequency Distributions with  Local Differential Privacy

Introduction: The amount of data generated by original research is growing
exponentially. Publicly releasing them is recommended to comply with the Open
Science principles. However, data collected from human participants cannot be
released as-is without raising privacy concerns. Fully synthetic data represent
a promising answer to this challenge. This approach is explored by the French
Centre de Recherche en {\'E}pid{\'e}miologie et Sant{\'e} des Populations in
the form of a synthetic data generation framework based on Classification and
Regression Trees and an original distance-based filtering. The goal of this
work was to develop a refined version of this framework and to assess its
risk-utility profile with empirical and formal tools, including novel ones
developed for the purpose of this evaluation.Materials and Methods: Our
synthesis framework consists of four successive steps, each of which is
designed to prevent specific risks of disclosure. We assessed its performance
by applying two or more of these steps to a rich epidemiological dataset.
Privacy and utility metrics were computed for each of the resulting synthetic
datasets, which were further assessed using machine learning
approaches.Results: Computed metrics showed a satisfactory level of protection
against attribute disclosure attacks for each synthetic dataset, especially
when the full framework was used. Membership disclosure attacks were formally
prevented without significantly altering the data. Machine learning approaches
showed a low risk of success for simulated singling out and linkability
attacks. Distributional and inferential similarity with the original data were
high with all datasets.Discussion: This work showed the technical feasibility
of generating publicly releasable synthetic data using a multi-step framework.
Formal and empirical tools specifically developed for this demonstration are a
valuable contribution to this field. Further research should focus on the
extension and validation of these tools, in an effort to specify the intrinsic
qualities of alternative data synthesis methods.Conclusion: By successfully
assessing the quality of data produced using a novel multi-step synthetic data
generation framework, we showed the technical and conceptual soundness of the
Open-CESP initiative, which seems ripe for full-scale implementation.

通过开发多步骤的合成数据生成框架并评估其风险效用模型，成功评估了使用该框架生成的数据的质量，展示了开放 - CESP 倡议的技术和概念的可行性。

一个基于距离的原创全合成数据生成方法的统计属性和隐私保证

Statistical properties and privacy guarantees of an original  distance-based fully synthetic data generation method

Conventional Learning-to-Rank (LTR) methods optimize the utility of the
rankings to the users, but they are oblivious to their impact on the ranked
items. However, there has been a growing understanding that the latter is
important to consider for a wide range of ranking applications (e.g. online
marketplaces, job placement, admissions). To address this need, we propose a
general LTR framework that can optimize a wide range of utility metrics (e.g.
NDCG) while satisfying fairness of exposure constraints with respect to the
items. This framework expands the class of learnable ranking functions to
stochastic ranking policies, which provides a language for rigorously
expressing fairness specifications. Furthermore, we provide a new LTR algorithm
called Fair-PG-Rank for directly searching the space of fair ranking policies
via a policy-gradient approach. Beyond the theoretical evidence in deriving the
framework and the algorithm, we provide empirical results on simulated and
real-world datasets verifying the effectiveness of the approach in individual
and group-fairness settings.

本文提出了一种利用随机排序策略来进行公平学习及考虑排序项影响的通用 LTR 框架，并通过基于政策梯度方法的 Fair-PG-Rank 算法进行优化，可在保持曝光公平性的情况下优化各种效用指标。通过实验结果验证了此方法在个人和集体公平性方面的有效性。