Mean estimation over numeric data with personalized local differential privacy
The fast development of the Internet and mobile devices results in a crowdsensing business
model, where individuals (users) are willing to contribute their data to help the institution …
model, where individuals (users) are willing to contribute their data to help the institution …
AAA: an Adaptive Mechanism for Locally Differential Private Mean Estimation
Local differential privacy (LDP) is a strong privacy standard that has been adopted by
popular software systems. The main idea is that each individual perturbs their own data …
popular software systems. The main idea is that each individual perturbs their own data …
A utility-optimized framework for personalized private histogram estimation
Recently, local differential privacy (LDP), as a strong and practical notion, has been applied
to deal with privacy issues in data collection. However, existing LDP-based strategies mainly …
to deal with privacy issues in data collection. However, existing LDP-based strategies mainly …
Local differential privacy for data collection and analysis
Abstract Local Differential Privacy (LDP) can provide each user with strong privacy
guarantees under untrusted data curators while ensuring accurate statistics derived from …
guarantees under untrusted data curators while ensuring accurate statistics derived from …
Collecting and analyzing multidimensional data with local differential privacy
Local differential privacy (LDP) is a recently proposed privacy standard for collecting and
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …
analyzing data, which has been used, eg, in the Chrome browser, iOS and macOS. In LDP …
Online local differential private quantile inference via self-normalization
Based on binary inquiries, we developed an algorithm to estimate population quantiles
under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides …
under Local Differential Privacy (LDP). By self-normalizing, our algorithm provides …
Pldp: Personalized local differential privacy for multidimensional data aggregation
The collection of multidimensional crowdsourced data has caused a public concern
because of the privacy issues. To address it, local differential privacy (LDP) is proposed to …
because of the privacy issues. To address it, local differential privacy (LDP) is proposed to …
Comparing population means under local differential privacy: with significance and power
A statistical hypothesis test determines whether a hypothesis should be rejected based on
samples from populations. In particular, randomized controlled experiments (or A/B testing) …
samples from populations. In particular, randomized controlled experiments (or A/B testing) …
Locally differentially private data collection and analysis
T Wang, J Zhao, X Yang, X Ren - arXiv preprint arXiv:1906.01777, 2019 - arxiv.org
Local differential privacy (LDP) can provide each user with strong privacy guarantees under
untrusted data curators while ensuring accurate statistics derived from privatized data. Due …
untrusted data curators while ensuring accurate statistics derived from privatized data. Due …
A comprehensive survey on local differential privacy toward data statistics and analysis
T Wang, X Zhang, J Feng, X Yang - Sensors, 2020 - mdpi.com
Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …