arXiv research

New methods for private statistical inference under local differential privacy.

Develops Merton's model for private companies using DDM.

Dynamic model considers private asset markets' complexities.

A framework for private causal effect estimation without structural assumptions.

Transforms robust algorithms into private ones with optimal error rates.

Private anchors affect how information is communicated and can improve or distort transmission.

Efficient methods estimate bid and value distributions in auctions.

We consider a network of agents that aim to learn some unknown state of the world using private observations and exchange of beliefs. At each time, agents observe private signals generated based on the true unknown state. Each agent might not be able to distinguish the true state based only on her private observations.…

Develops a computationally tractable differentially private mean estimator called the balloon mean.

Private learning can perform well in high dimensions, contrary to known results.

Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but…

Study evaluates early-stage cybersecurity firms' performance using Crunchbase data.

Develops privacy-preserving methods for longitudinal linear regression.

We compare the sample complexity of private learning [Kasiviswanathan et al. 2008] and sanitization~[Blum et al. 2008] under pure $ε$-differential privacy [Dwork et al. TCC 2006] and approximate $(ε,δ)$-differential privacy [Dwork et al. Eurocrypt 2006]. We show that the sample complexity of these tasks under approxima…

AdaDPSyn generates synthetic examples to protect private data in ICL.

Paper addresses privacy and robustness in stochastic linear bandits.

Distributed estimation and learning with privacy preserved.

Differentially private synthetic control estimates treatment effects while protecting privacy.

Study on markets with insiders receiving private signals affecting asset prices and information flow.

This work investigates the case of a network of agents that attempt to learn some unknown state of the world amongst the finitely many possibilities. At each time step, agents all receive random, independently distributed private signals whose distributions are dependent on the unknown state of the world. However, it m…

Paper uses relaxation techniques to find optimal brokerage fees with private signals.

Bayesian optimization is a powerful tool for fine-tuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization bec…

Differentially private method for estimating individualized treatment rules.

When banks choose similar investment strategies the financial system becomes vulnerable to common shocks. We model a simple financial system in which banks decide about their investment strategy based on a private belief about the state of the world and a social belief formed from observing the actions of peers. Observ…

Paper develops methods for fair insurance pricing without direct access to sensitive attributes.

This study examines whether tokenized assets improve liquidity and finds significant differences across categories.

New method disentangles shared and private latent factors in multimodal data.

New algorithm learns optimal policies in strategic MDPs with private types.

While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular approach known as **differential privacy** offers provable privacy guarantees, but it is often observed in practice that it could substanti…

Multi-modal data collections, such as corpora of paired images and text snippets, require analysis methods beyond single-view component and topic models. For continuous observations the current dominant approach is based on extensions of canonical correlation analysis, factorizing the variation into components shared b…

Privacy improves robustness in statistical estimation.

Solves a game between brokers and informed traders using stochastic differential equations.

Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains, such as medical diagnostics. In this paper we present an algorithm for differentially private learning of the parameters of a DGM with a publicly known graph structure over fully obser…

A network of agents attempt to learn some unknown state of the world drawn by nature from a finite set. Agents observe private signals conditioned on the true state, and form beliefs about the unknown state accordingly. Each agent may face an identification problem in the sense that she cannot distinguish the truth in …

Paper explores privacy in machine learning models using ICL.

SOTERIA optimizes neural networks for secure inference with minimal overhead.

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our …

This paper studies trade-offs in private prediction methods.

Private method measures nonlinear correlations between data hosted across two entities.

Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents' observations are al…

In this paper we develop the first algorithms for online submodular minimization that preserve differential privacy under full information feedback and bandit feedback. A sequence of $T$ submodular functions over a collection of $n$ elements arrive online, and at each timestep the algorithm must choose a subset of $[n]…

Contextual bandit algorithms~(CBAs) often rely on personal data to provide recommendations. Centralized CBA agents utilize potentially sensitive data from recent interactions to provide personalization to end-users. Keeping the sensitive data locally, by running a local agent on the user's device, protects the user's p…

Following the approach of standard filtering theory, we analyse investor-valuation of firms, when these are modelled as geometric-Brownian state processes that are privately and partially observed, at random (Poisson) times, by agents. Tasked with disclosing forecast values, agents are able purposefully to withhold the…

CryptoNAS improves PI accuracy by 3.4% with 2.4x less latency.

New algorithms for privately learning decision lists and halfspaces.

Private PGB boosts synthetic data quality using GANs and privacy techniques.

Algorithm learns optimal coordination for strategic agents in uncertain settings.

This paper addresses privacy in federated learning with wireless clients and base stations.