3 research outputs found
Conditional Analysis for Key-Value Data with Local Differential Privacy
Local differential privacy (LDP) has been deemed as the de facto measure for
privacy-preserving distributed data collection and analysis. Recently,
researchers have extended LDP to the basic data type in NoSQL systems: the
key-value data, and show its feasibilities in mean estimation and frequency
estimation. In this paper, we develop a set of new perturbation mechanisms for
key-value data collection and analysis under the strong model of local
differential privacy. Since many modern machine learning tasks rely on the
availability of conditional probability or the marginal statistics, we then
propose the conditional frequency estimation method for key analysis and the
conditional mean estimation for value analysis in key-value data. The released
statistics with conditions can further be used in learning tasks. Extensive
experiments of frequency and mean estimation on both synthetic and real-world
datasets validate the effectiveness and accuracy of the proposed key-value
perturbation mechanisms against the state-of-art competitors
Local Differential Privacy and Its Applications: A Comprehensive Survey
With the fast development of Information Technology, a tremendous amount of
data have been generated and collected for research and analysis purposes. As
an increasing number of users are growing concerned about their personal
information, privacy preservation has become an urgent problem to be solved and
has attracted significant attention. Local differential privacy (LDP), as a
strong privacy tool, has been widely deployed in the real world in recent
years. It breaks the shackles of the trusted third party, and allows users to
perturb their data locally, thus providing much stronger privacy protection.
This survey provides a comprehensive and structured overview of the local
differential privacy technology. We summarise and analyze state-of-the-art
research in LDP and compare a range of methods in the context of answering a
variety of queries and training different machine learning models. We discuss
the practical deployment of local differential privacy and explore its
application in various domains. Furthermore, we point out several research
gaps, and discuss promising future research directions.Comment: 24 page
A Comprehensive Survey on Local Differential Privacy Toward Data Statistics and Analysis
Collecting and analyzing massive data generated from smart devices have
become increasingly pervasive in crowdsensing, which are the building blocks
for data-driven decision-making. However, extensive statistics and analysis of
such data will seriously threaten the privacy of participating users. Local
differential privacy (LDP) has been proposed as an excellent and prevalent
privacy model with distributed architecture, which can provide strong privacy
guarantees for each user while collecting and analyzing data. LDP ensures that
each user's data is locally perturbed first in the client-side and then sent to
the server-side, thereby protecting data from privacy leaks on both the
client-side and server-side. This survey presents a comprehensive and
systematic overview of LDP with respect to privacy models, research tasks,
enabling mechanisms, and various applications. Specifically, we first provide a
theoretical summarization of LDP, including the LDP model, the variants of LDP,
and the basic framework of LDP algorithms. Then, we investigate and compare the
diverse LDP mechanisms for various data statistics and analysis tasks from the
perspectives of frequency estimation, mean estimation, and machine learning.
What's more, we also summarize practical LDP-based application scenarios.
Finally, we outline several future research directions under LDP.Comment: 28page