Graph data anonymization, de-anonymization attacks, and de-anonymizability quantification: A survey
Nowadays, many computer and communication systems generate graph data. Graph data
span many different domains, ranging from online social network data from networks like …
span many different domains, ranging from online social network data from networks like …
Privacy preservation in big data from the communication perspective—A survey
T Wang, Z Zheng, MH Rehmani… - … Surveys & Tutorials, 2018 - ieeexplore.ieee.org
The advancement of data communication technologies promotes widespread data collection
and transmission in various application domains, thereby expanding big data significantly …
and transmission in various application domains, thereby expanding big data significantly …
Local and central differential privacy for robustness and privacy in federated learning
Federated Learning (FL) allows multiple participants to train machine learning models
collaboratively by keeping their datasets local while only exchanging model updates. Alas …
collaboratively by keeping their datasets local while only exchanging model updates. Alas …
Towards practical differential privacy for SQL queries
Differential privacy promises to enable general data analytics while protecting individual
privacy, but existing differential privacy mechanisms do not support the wide variety of …
privacy, but existing differential privacy mechanisms do not support the wide variety of …
Powerlyra: Differentiated graph computation and partitioning on skewed graphs
R Chen, J Shi, Y Chen, B Zang, H Guan… - ACM Transactions on …, 2019 - dl.acm.org
Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …
Generating synthetic decentralized social graphs with local differential privacy
A large amount of valuable information resides in decentralized social graphs, where no
entity has access to the complete graph structure. Instead, each user maintains locally a …
entity has access to the complete graph structure. Instead, each user maintains locally a …
Differential privacy and machine learning: a survey and review
The objective of machine learning is to extract useful information from data, while privacy is
preserved by concealing information. Thus it seems hard to reconcile these competing …
preserved by concealing information. Thus it seems hard to reconcile these competing …
Linkteller: Recovering private edges from graph neural networks via influence analysis
Graph structured data have enabled several successful applications such as
recommendation systems and traffic prediction, given the rich node features and edges …
recommendation systems and traffic prediction, given the rich node features and edges …
Applications of differential privacy in social network analysis: A survey
Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …
As social network analysis has been enjoying many applications, it opens a new arena for …
Publishing graph degree distribution with node differential privacy
Graph data publishing under node-differential privacy (node-DP) is challenging due to the
huge sensitivity of queries. However, since a node in graph data oftentimes represents a …
huge sensitivity of queries. However, since a node in graph data oftentimes represents a …