Part 5: Privacy (Short Paper)International audienceIn this paper, we propose new ideas to protect user privacy while allowing the use of a user history graph. We define new privacy notions for user history graphs and consider algorithms to generate a privacy-preserving digraph from the original graph
As data collection and storage techniques being greatly improved, data analysis is becoming an incre...
In this paper we review the state of the art on graph privacy with special emphasis on applications ...
In the real world, many phenomena can be naturally modeled as a graph whose nodes represent entities...
The privacy protection of graph data has become more and more important in recent years. Many works ...
Nowadays, more and more people join social networks, such as Facebook, Linkedin, and Livespace, to s...
The privacy protection of graph data has become more and more important in recent years. Many works ...
abstract: The explosive Web growth in the last decade has drastically changed the way billions of pe...
In graph machine learning, data collection, sharing, and analysis often involve multiple parties, ea...
Abstract In this article, we present a privacy-preserving technique for user-centric multi-release ...
In this paper we review the state of the art on graph privacy with special emphasis on applications ...
Part 5: Privacy-Preserving TechnologiesInternational audienceGraph structured data can be found in m...
In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Ou...
In the real world, graph structured data is ubiquitous. For example, social networks, communications...
A new graph database model is introduced that allows for an efficient and straightforward privacy-pr...
Currently, individuals leave a digital trace of their activities when they use their smartphones, so...
As data collection and storage techniques being greatly improved, data analysis is becoming an incre...
In this paper we review the state of the art on graph privacy with special emphasis on applications ...
In the real world, many phenomena can be naturally modeled as a graph whose nodes represent entities...
The privacy protection of graph data has become more and more important in recent years. Many works ...
Nowadays, more and more people join social networks, such as Facebook, Linkedin, and Livespace, to s...
The privacy protection of graph data has become more and more important in recent years. Many works ...
abstract: The explosive Web growth in the last decade has drastically changed the way billions of pe...
In graph machine learning, data collection, sharing, and analysis often involve multiple parties, ea...
Abstract In this article, we present a privacy-preserving technique for user-centric multi-release ...
In this paper we review the state of the art on graph privacy with special emphasis on applications ...
Part 5: Privacy-Preserving TechnologiesInternational audienceGraph structured data can be found in m...
In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Ou...
In the real world, graph structured data is ubiquitous. For example, social networks, communications...
A new graph database model is introduced that allows for an efficient and straightforward privacy-pr...
Currently, individuals leave a digital trace of their activities when they use their smartphones, so...
As data collection and storage techniques being greatly improved, data analysis is becoming an incre...
In this paper we review the state of the art on graph privacy with special emphasis on applications ...
In the real world, many phenomena can be naturally modeled as a graph whose nodes represent entities...