arXiv subjects

Wanrong Zhang

Publications and source records attributed to Wanrong Zhang.

23 records · Page 2Linked to original sources

arXiv2021

Attribute Privacy: Framework and Mechanisms

Ensuring the privacy of training data is a growing concern since many machine learning models are trained on confidential and potentially sensitive data. Much attention has been devoted to methods for protecting individual privacy during analyses of large datasets. However in many settings, global properties of the dataset may also be sensitive (e.g., mortality rate in a hospital rather than presence of a particular patient in the dataset). In this work, we depart from individual privacy to initiate the study of attribute privacy, where a data owner is concerned about revealing sensitive properties of a whole dataset during analysis. We propose definitions to capture \emph{attribute privacy} in two relevant cases where global attributes may need to be protected: (1) properties of a specific dataset and (2) parameters of the underlying distribution from which dataset is sampled. We also provide two efficient mechanisms and one inefficient mechanism that satisfy attribute privacy for these settings. We base our results on a novel use of the Pufferfish framework to account for correlations across attributes in the data, thus addressing "the challenging problem of developing Pufferfish instantiations and algorithms for general aggregate secrets" that was left open by \cite{kifer2014pufferfish}.

cs.CR↗

arXiv2020

PAPRIKA: Private Online False Discovery Rate Control

In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability of a false discovery increases as more tests are performed. Thus the problem of False Discovery Rate (FDR) control is to find a procedure for testing multiple hypotheses that accounts for this effect in determining the set of hypotheses to reject. The goal is to minimize the number (or fraction) of false discoveries, while maintaining a high true positive rate (i.e., correct discoveries). In this work, we study False Discovery Rate (FDR) control in multiple hypothesis testing under the constraint of differential privacy for the sample. Unlike previous work in this direction, we focus on the online setting, meaning that a decision about each hypothesis must be made immediately after the test is performed, rather than waiting for the output of all tests as in the offline setting. We provide new private algorithms based on state-of-the-art results in non-private online FDR control. Our algorithms have strong provable guarantees for privacy and statistical performance as measured by FDR and power. We also provide experimental results to demonstrate the efficacy of our algorithms in a variety of data environments.

stat.ML↗

arXiv2020

Privately detecting changes in unknown distributions

The change-point detection problem seeks to identify distributional changes in streams of data. Increasingly, tools for change-point detection are applied in settings where data may be highly sensitive and formal privacy guarantees are required, such as identifying disease outbreaks based on hospital records, or IoT devices detecting activity within a home. Differential privacy has emerged as a powerful technique for enabling data analysis while preventing information leakage about individuals. Much of the prior work on change-point detection---including the only private algorithms for this problem---requires complete knowledge of the pre-change and post-change distributions. However, this assumption is not realistic for many practical applications of interest. This work develops differentially private algorithms for solving the change-point problem when the data distributions are unknown. Additionally, the data may be sampled from distributions that change smoothly over time, rather than fixed pre-change and post-change distributions. We apply our algorithms to detect changes in the linear trends of such data streams. Finally, we also provide experimental results to empirically validate the performance of our algorithms.

math.ST↗

arXiv2018

Differentially Private Change-Point Detection

The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data. This problem appears in many important practical settings involving personal data, including biosurveillance, fault detection, finance, signal detection, and security systems. The field of differential privacy offers data analysis tools that provide powerful worst-case privacy guarantees. We study the statistical problem of change-point detection through the lens of differential privacy. We give private algorithms for both online and offline change-point detection, analyze these algorithms theoretically, and provide empirical validation of our results.

math.ST↗

arXiv2012

A Novel Low Power UWB Cascode SiGe BiCMOS LNA with Current Reuse and Zero-Pole Cancellation

A low power cascode SiGe BiCMOS low noise amplifier (LNA) with current reuse and zero-pole cancellation is presented for ultra-wideband (UWB) application. The LNA is composed of cascode input stage and common emitter (CE) output stage with dual loop feedbacks. The novel cascode-CE current reuse topology replaces the traditional two stages topology so as to obtain low power consumption. The emitter degenerative inductor in input stage is adopted to achieve good input impedance matching and noise performance. The two poles are introduced by the emitter inductor, which will degrade the gain performance, are cancelled by the dual loop feedbacks of the resistance-inductor (RL) shunt-shunt feedback and resistance-capacitor (RC) series-series feedback in the output stage. Meanwhile, output impedance matching is also achieved. Based on TSMC 0.35μm SiGe BiCMOS process, the topology and chip layout of the proposed LNA are designed and post-simulated. The LNA achieves the noise figure of 2.3~4.1dB, gain of 18.9~20.2dB, gain flatness of \pm0.65dB, input third order intercept point (IIP3) of -7dBm at 6GHz, exhibits less than 16ps of group delay variation, good input and output impedances matching, and unconditionally stable over the whole band. The power consuming is only 18mW.

cs.OH↗