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A comprehensive survey on local differential privacy
With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …
Privacy-preserving distributed optimization via subspace perturbation: A general framework
As the modern world becomes increasingly digitized and interconnected, distributed signal
processing has proven to be effective in processing its large volume of data. However, a …
processing has proven to be effective in processing its large volume of data. However, a …
Privacy-preserving distributed processing: Metrics, bounds and algorithms
Privacy-preserving distributed processing has recently attracted considerable attention. It
aims to design solutions for conducting signal processing tasks over networks in a …
aims to design solutions for conducting signal processing tasks over networks in a …
Sarve: synthetic data and local differential privacy for private frequency estimation
The collection of user attributes by service providers is a double-edged sword. They are
instrumental in driving statistical analysis to train more accurate predictive models like …
instrumental in driving statistical analysis to train more accurate predictive models like …
Provable privacy advantages of decentralized federated learning via distributed optimization
Federated learning (FL) emerged as a paradigm designed to improve data privacy by
enabling data to reside at its source, thus embedding privacy as a core consideration in FL …
enabling data to reside at its source, thus embedding privacy as a core consideration in FL …
[HTML][HTML] Communication efficient privacy-preserving distributed optimization using adaptive differential quantization
Privacy issues and communication cost are both major concerns in distributed optimization
in networks. There is often a trade-off between them because the encryption methods used …
in networks. There is often a trade-off between them because the encryption methods used …
A privacy-preserving game model for local differential privacy by using information-theoretic approach
N Wu, C Peng, K Niu - IEEE Access, 2020 - ieeexplore.ieee.org
Local differential privacy (LDP) is an effective privacy-preserving model to address the
problems which do not have a trusted entity. The main idea of the LDP is to add randomness …
problems which do not have a trusted entity. The main idea of the LDP is to add randomness …
Gdp vs. ldp: A survey from the perspective of information-theoretic channel
The existing work has conducted in-depth research and analysis on global differential
privacy (GDP) and local differential privacy (LDP) based on information theory. However, the …
privacy (GDP) and local differential privacy (LDP) based on information theory. However, the …
Fisher information as a utility metric for frequency estimation under local differential privacy
Local Differential Privacy (LDP) is the de facto standard technique to ensure privacy for
users whose data is collected by a data aggregator they do not necessarily trust. This …
users whose data is collected by a data aggregator they do not necessarily trust. This …
The privacy funnel from the viewpoint of local differential privacy
We consider a database $\vec {X}=(X_1,\cdots, X_n) $ containing the data of $ n $ users.
The data aggregator wants to publicise the database, but wishes to sanitise the dataset to …
The data aggregator wants to publicise the database, but wishes to sanitise the dataset to …