What is he best known for?
The fields of study he is best known for:
- The Internet
- Operating system
- Computer security
His primary areas of investigation include Computer security, Negotiation, Access control, Internet privacy and Digital credential.
His studies in Computer security integrate themes in fields like Pointing device and Data science.
He undertakes multidisciplinary investigations into Negotiation and Limiting in his work.
His Access control study incorporates themes from Security policy, Interpretation and Metamodeling.
His Internet privacy research incorporates themes from Information sensitivity, The Internet, Authorization and Data Protection Act 1998.
Ting Yu interconnects Trust management and Interoperability in the investigation of issues within Digital credential.
His most cited work include:
- Aggregate Query Answering on Anonymized Tables (308 citations)
- Supporting structured credentials and sensitive policies through interoperable strategies for automated trust negotiation (287 citations)
- Differentially Private Spatial Decompositions (264 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Computer security, Access control, Data mining, Theoretical computer science and Differential privacy.
Within one scientific family, Ting Yu focuses on topics pertaining to Internet privacy under Computer security, and may sometimes address concerns connected to World Wide Web and Authorization.
His work on Role-based access control as part of general Access control research is often related to Negotiation and Obligation, thus linking different fields of science.
His research integrates issues of Microdata, Graph, Set and Cluster analysis in his study of Data mining.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Soundness and Encryption.
His work carried out in the field of Differential privacy brings together such families of science as Graph database and Information privacy.
He most often published in these fields:
- Computer security (34.69%)
- Access control (19.73%)
- Data mining (14.97%)
What were the highlights of his more recent work (between 2016-2021)?
- Computer security (34.69%)
- Differential privacy (12.93%)
- Data mining (14.97%)
In recent papers he was focusing on the following fields of study:
Ting Yu spends much of his time researching Computer security, Differential privacy, Data mining, Domain and Information sensitivity.
Many of his research projects under Computer security are closely connected to Scale with Scale, tying the diverse disciplines of science together.
His biological study spans a wide range of topics, including Machine learning, Theoretical computer science, Social graph and Artificial intelligence.
Ting Yu focuses mostly in the field of Theoretical computer science, narrowing it down to topics relating to Set and, in certain cases, Information privacy and Bloom filter.
The concepts of his Data mining study are interwoven with issues in Graph, Scalability, Key and Inference.
Ting Yu has included themes like Security policy, Information leakage, Data access, Crowdsourcing and Table in his Information sensitivity study.
Between 2016 and 2021, his most popular works were:
- Generating Synthetic Decentralized Social Graphs with Local Differential Privacy (61 citations)
- A Survey on Malicious Domains Detection through DNS Data Analysis (46 citations)
- Analyzing Subgraph Statistics from Extended Local Views with Decentralized Differential Privacy (14 citations)
In his most recent research, the most cited papers focused on:
- The Internet
- Operating system
- Database
His primary areas of investigation include Data mining, Differential privacy, Graph, Computer security and Inference.
He has researched Differential privacy in several fields, including Information sensitivity and Social graph.
His research in Information sensitivity intersects with topics in Theoretical computer science and Private information retrieval.
His work in the fields of Graph, such as Graph inference, overlaps with other areas such as Association scheme and Belief propagation.
His study in Computer security is interdisciplinary in nature, drawing from both The Internet and Data analysis.
The various areas that he examines in his Inference study include Crowdsourcing and Machine learning.
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