A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Privacy is enhanced by synthetic data release even with unlimited data.
User releases data to service provider while balancing privacy and utility.
GRAND ensures node-level differential privacy for network data.
New algorithms improve privacy-preserving data release using external predictions.
New algorithm maintains privacy while improving model performance in selective release.
We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an esti…
New framework predicts earnings announcements using press release content, surpassing earnings surprises.
This paper provides a method for noise-calibrated inference from DP synthetic data.
Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanis…
The ability to analyze and forecast stratospheric weather conditions is fundamental to addressing climate change. However, our capacity to collect data in the stratosphere is limited by sparsely deployed weather balloons. We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor device…
New algorithm corrects bias in LDP-released data for better analysis.
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
A new method for releasing AI workflows to avoid premature incorrect results.
This paper documents the release of the ELKI data mining framework, version 0.7.5. ELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. In order to achieve high performance a…
This work addresses privacy issues in IoT data sharing by balancing information disclosure and user privacy.
Designing a data sharing mechanism without sacrificing too much privacy can be considered as a game between data holders and malicious attackers. This paper describes a compressive adversarial privacy framework that captures the trade-off between the data privacy and utility. We characterize the optimal data releasing …
Automates phased release strategy to balance risk and speed.
Three new oracle-efficient algorithms for private synthetic data release.
The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influ…
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. W…
Study shows monetary policy impacts digital assets like BTC and ETH.
Private release of sensitive data enables fair learning.
Study private query release with public data, reducing sample sizes.
P3GM improves privacy-preserving data synthesis for high-dimensional data.
Among other macroeconomic indicators, the monthly release of U.S. unemployment rate figures in the Employment Situation report by the U.S. Bureau of Labour Statistics gets a lot of media attention and strongly affects the stock markets. I investigate whether a profitable investment strategy can be constructed by predic…
CET model uses contrastive learning to improve earnings data predictions.
Smart Meters (SMs) are able to share the power consumption of users with utility providers almost in real-time. These fine-grained signals carry sensitive information about users, which has raised serious concerns from the privacy viewpoint. In this paper, we focus on real-time privacy threats, i.e., potential attacker…
Differential privacy of Gaussian process posterior sampling
mvlearn simplifies multiview machine learning for non-specialists.
Paper addresses private online convex optimization with optimal algorithms in various geometries and high-dimensional bandits.
BioFinBERT analyzes sentiment of biotech press releases and financial text around inflection points.
Synthetic data can amplify privacy in linear regression models.
XAI-Bench releases synthetic datasets for evaluating feature attribution methods.
Differential privacy is a framework for privately releasing summaries of a database. Previous work has focused mainly on methods for which the output is a finite dimensional vector, or an element of some discrete set. We develop methods for releasing functions while preserving differential privacy. Specifically, we sho…
New method for estimating median and mean with high probability privacy.
Gen-LRA attacks synthetic data leakage without model knowledge.
With the proliferation of mobile devices and the internet of things, developing principled solutions for privacy in time series applications has become increasingly important. While differential privacy is the gold standard for database privacy, many time series applications require a different kind of guarantee, and a…
A new method for private query release using Johnson-Lindenstrauss projection.
Study investigates micro-event detection on FLOSS version releases from Stack Overflow.
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Pac…
Framework for efficient statistical estimation with privacy guarantees.
Many commonly used learning algorithms work by iteratively updating an intermediate solution using one or a few data points in each iteration. Analysis of differential privacy for such algorithms often involves ensuring privacy of each step and then reasoning about the cumulative privacy cost of the algorithm. This is …
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately b…
Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a…
We consider the problem of publicly releasing a dataset for support vector machine classification while not infringing on the privacy of data subjects (i.e., individuals whose private information is stored in the dataset). The dataset is systematically obfuscated using an additive noise for privacy protection. Motivate…
The study provides a practical strategy for pricing and hedging equity-release mortgages guarantees.
What makes a paper independently reproducible? Debates on reproducibility center around intuition or assumptions but lack empirical results. Our field focuses on releasing code, which is important, but is not sufficient for determining reproducibility. We take the first step toward a quantifiable answer by manually att…