Timezone: »
Privacy Amplification by Subsampling in Time Domain
Tatsuki Koga · Casey M Meehan · Kamalika Chaudhuri
Releasing temporal aggregate signals with enough privacy guarantees is still tricky despite their wide applications and impact on society. The main difficulty lies in their sensitivity which scales linearly with the signal length. We analyze that one can reduce the sensitivity by subsampling in the time domain under reasonable assumptions. Then, based on the analysis, we propose a differentially private algorithm that utilizes signal subsampling and filtering. We demonstrate the utility gain of our algorithm empirically with the real and synthetic signals.
Author Information
Tatsuki Koga (University of California, San Diego)
Casey M Meehan (UCSD)
Kamalika Chaudhuri (University of California at San Diego)
More from the Same Authors
-
2021 : Understanding Instance-based Interpretability of Variational Auto-Encoders »
· Zhifeng Kong · Kamalika Chaudhuri -
2021 : Privacy Amplification by Bernoulli Sampling »
Jacob Imola · Kamalika Chaudhuri -
2021 : A Shuffling Framework For Local Differential Privacy »
Casey M Meehan · Amrita Roy Chowdhury · Kamalika Chaudhuri · Somesh Jha -
2022 : Understanding Rare Spurious Correlations in Neural Networks »
Yao-Yuan Yang · Chi-Ning Chou · Kamalika Chaudhuri -
2022 Poster: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Spotlight: Thompson Sampling for Robust Transfer in Multi-Task Bandits »
Zhi Wang · Chicheng Zhang · Kamalika Chaudhuri -
2022 Poster: Bounding Training Data Reconstruction in Private (Deep) Learning »
Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten -
2022 Oral: Bounding Training Data Reconstruction in Private (Deep) Learning »
Chuan Guo · Brian Karrer · Kamalika Chaudhuri · Laurens van der Maaten -
2021 : Discussion Panel #2 »
Bo Li · Nicholas Carlini · Andrzej Banburski · Kamalika Chaudhuri · Will Xiao · Cihang Xie -
2021 : Invited Talk #9 »
Kamalika Chaudhuri -
2021 : Invited Talk: Kamalika Chaudhuri »
Kamalika Chaudhuri -
2021 : Invited Talk: Kamalika Chaudhuri »
Kamalika Chaudhuri -
2021 : Live Panel Discussion »
Thomas Dietterich · Chelsea Finn · Kamalika Chaudhuri · Yarin Gal · Uri Shalit -
2021 Poster: Sample Complexity of Robust Linear Classification on Separated Data »
Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri -
2021 Spotlight: Sample Complexity of Robust Linear Classification on Separated Data »
Robi Bhattacharjee · Somesh Jha · Kamalika Chaudhuri -
2021 Poster: Connecting Interpretability and Robustness in Decision Trees through Separation »
Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri -
2021 Spotlight: Connecting Interpretability and Robustness in Decision Trees through Separation »
Michal Moshkovitz · Yao-Yuan Yang · Kamalika Chaudhuri -
2020 Poster: When are Non-Parametric Methods Robust? »
Robi Bhattacharjee · Kamalika Chaudhuri -
2019 Talk: Opening Remarks »
Kamalika Chaudhuri · Ruslan Salakhutdinov -
2018 Poster: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Poster: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
Yizhen Wang · Somesh Jha · Kamalika Chaudhuri -
2018 Oral: Active Learning with Logged Data »
Songbai Yan · Kamalika Chaudhuri · Tara Javidi -
2018 Oral: Analyzing the Robustness of Nearest Neighbors to Adversarial Examples »
Yizhen Wang · Somesh Jha · Kamalika Chaudhuri -
2017 Workshop: Picky Learners: Choosing Alternative Ways to Process Data. »
Corinna Cortes · Kamalika Chaudhuri · Giulia DeSalvo · Ningshan Zhang · Chicheng Zhang -
2017 Poster: Active Heteroscedastic Regression »
Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan -
2017 Talk: Active Heteroscedastic Regression »
Kamalika Chaudhuri · Prateek Jain · Nagarajan Natarajan