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Ruobin Gong

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4 papers

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ISIPTA Conference 2025 Conference Paper

Elicitation for sets of probabilities and distributions

Mark J. Schervish
Teddy Seidenfeld
Ruobin Gong
Joseph B. Kadane
Rafael B. Stern

We investigate techniques for applying strictly proper scoring rules to elicit arbitrary sets of probabilities for an event and for eliciting sets of countably additive (finite dimensional) joint distributions. We contrast <em>E</em>-admissibility, Maximality, and $\Gamma$-Maximin as three IP decision rules for these elicitations. The techniques we investigate apply with sets of probabilities that need not be convex or even connected, and with distributions that may lack moments. We address some challenges to applying these techniques for eliciting merely finitely additive probability distributions.

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AAAI Conference 2023 Conference Paper

Integer Subspace Differential Privacy

Prathamesh Dharangutte
Jie Gao
Ruobin Gong
Fang-Yi Yu

We propose new differential privacy solutions for when external invariants and integer constraints are simultaneously enforced on the data product. These requirements arise in real world applications of private data curation, including the public release of the 2020 U.S. Decennial Census. They pose a great challenge to the production of provably private data products with adequate statistical usability. We propose integer subspace differential privacy to rigorously articulate the privacy guarantee when data products maintain both the invariants and integer characteristics, and demonstrate the composition and post-processing properties of our proposal. To address the challenge of sampling from a potentially highly restricted discrete space, we devise a pair of unbiased additive mechanisms, the generalized Laplace and the generalized Gaussian mechanisms, by solving the Diophantine equations as defined by the constraints. The proposed mechanisms have good accuracy, with errors exhibiting sub-exponential and sub-Gaussian tail probabilities respectively. To implement our proposal, we design an MCMC algorithm and supply empirical convergence assessment using estimated upper bounds on the total variation distance via L-lag coupling. We demonstrate the efficacy of our proposal with applications to a synthetic problem with intersecting invariants, a sensitive contingency table with known margins, and the 2010 Census county-level demonstration data with mandated fixed state population totals.

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NeurIPS Conference 2022 Conference Paper

Data Augmentation MCMC for Bayesian Inference from Privatized Data

Nianqiao Ju
Jordan Awan
Ruobin Gong
Vinayak Rao

Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential data. Specifically, the likelihood function of the privatized data requires integrating over the large space of confidential databases and is typically intractable. For Bayesian analysis, this results in a posterior distribution that is doubly intractable, rendering traditional MCMC techniques inapplicable. We propose an MCMC framework to perform Bayesian inference from the privatized data, which is applicable to a wide range of statistical models and privacy mechanisms. Our MCMC algorithm augments the model parameters with the unobserved confidential data, and alternately updates each one. For the potentially challenging step of updating the confidential data, we propose a generic approach that exploits the privacy guarantee of the mechanism to ensure efficiency. We give results on the computational complexity, acceptance rate, and mixing properties of our MCMC. We illustrate the efficacy and applicability of our methods on a naïve-Bayes log-linear model and on a linear regression model.

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AAAI Conference 2022 Conference Paper

Subspace Differential Privacy

Jie Gao
Ruobin Gong
Fang-Yi Yu

Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized data product as a primary utility requirement. Invariants challenge the formulation, implementation and interpretation of privacy guarantees. We propose subspace differential privacy, to honestly characterize the dependence of the sanitized output on confidential aspects of the data. We discuss two design frameworks that convert well-known differentially private mechanisms, such as the Gaussian and the Laplace mechanisms, to subspace differentially private ones that respect the invariants specified by the curator. For linear queries, we discuss the design of near optimal mechanisms that minimize the mean squared error. Subspace differentially private mechanisms rid the need for post-processing due to invariants, preserve transparency and statistical intelligibility of the output, and can be suitable for distributed implementation. We showcase the proposed mechanisms on the 2020 Census Disclosure Avoidance demonstration data, and a spatio-temporal dataset of mobile access point connections on a large university campus.

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