What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- The Internet
His primary areas of study are Data mining, Differential privacy, Internet privacy, Information privacy and Context.
His studies deal with areas such as Timestamp and Theoretical computer science as well as Data mining.
His study looks at the relationship between Differential privacy and fields such as Privacy software, as well as how they intersect with chemical problems.
He works mostly in the field of Internet privacy, limiting it down to concerns involving Peer-to-peer and, occasionally, Variety.
His study on Privacy by Design is often connected to Data sharing, Outsourcing and Data aggregator as part of broader study in Information privacy.
Context is connected with Credibility and Synthetic data in his research.
His most cited work include:
- PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities (1593 citations)
- Advances and Open Problems in Federated Learning (571 citations)
- A reputation-based trust model for peer-to-peer ecommerce communities. (316 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Differential privacy, Information privacy, Computer security and Scalability.
His work on Aggregate as part of general Data mining study is frequently connected to Record linkage, Data collection and Context, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Differential privacy study also includes fields such as
- Privacy by Design most often made with reference to Privacy software,
- Theoretical computer science which intersects with area such as Skyline.
His Information privacy research is multidisciplinary, incorporating elements of Data science, Secure multi-party computation, Data publishing and Distributed database.
Many of his research projects under Computer security are closely connected to Event with Event, tying the diverse disciplines of science together.
His Scalability research incorporates themes from Distributed computing, Privacy preserving and Protocol.
He most often published in these fields:
- Data mining (33.64%)
- Differential privacy (30.41%)
- Information privacy (17.05%)
What were the highlights of his more recent work (between 2018-2021)?
- Differential privacy (30.41%)
- Artificial intelligence (8.29%)
- Data mining (33.64%)
In recent papers he was focusing on the following fields of study:
Li Xiong focuses on Differential privacy, Artificial intelligence, Data mining, Computer security and Algorithm.
His Differential privacy research focuses on subjects like Internet privacy, which are linked to End-to-end principle.
His work is dedicated to discovering how Artificial intelligence, Machine learning are connected with Robustness, Generative grammar, Inference and Crowdsourcing and other disciplines.
In most of his Data mining studies, his work intersects topics such as Information privacy.
In his work, Adversary is strongly intertwined with Location-based service, which is a subfield of Computer security.
His studies examine the connections between Algorithm and genetics, as well as such issues in Skyline, with regards to Pareto optimal, Cluster analysis, Time complexity, Theoretical computer science and Scalability.
Between 2018 and 2021, his most popular works were:
- Advances and Open Problems in Federated Learning (571 citations)
- Quantifying Differential Privacy in Continuous Data Release Under Temporal Correlations (34 citations)
- Advances and Open Problems in Federated Learning (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Machine learning
Differential privacy, Data mining, Data collection, Computer security and Information privacy are his primary areas of study.
As part of his studies on Differential privacy, he frequently links adjacent subjects like Theoretical computer science.
The concepts of his Data mining study are interwoven with issues in Database server and Encoding.
His Adversary and Information sensitivity study, which is part of a larger body of work in Computer security, is frequently linked to Sequence and Event, bridging the gap between disciplines.
His Information privacy study incorporates themes from Spatial query and Server-side.
His Protocol course of study focuses on Skyline and Server, Encryption and Scalability.
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