What is he best known for?
The fields of study he is best known for:
- Algorithm
- Statistics
- Artificial intelligence
His primary scientific interests are in Theoretical computer science, Differential privacy, Algorithm, Function and Data mining.
His Theoretical computer science research is multidisciplinary, relying on both Random oracle, Instance-based learning, Statistical database and Secure two-party computation.
His research in Random oracle intersects with topics in Homomorphic encryption, Attribute-based encryption, Oblivious transfer and Zero-knowledge proof.
His Differential privacy study combines topics in areas such as Probably approximately correct learning, Algorithmic learning theory, Concept class, Local algorithm and Optimization problem.
His work in Algorithm covers topics such as Row which are related to areas like Sublinear function and Query optimization.
His work carried out in the field of Data mining brings together such families of science as Efficient algorithm, Cluster analysis, Information privacy and Minimum spanning tree.
His most cited work include:
- Calibrating noise to sensitivity in private data analysis (3875 citations)
- Evaluating 2-DNF formulas on ciphertexts (1252 citations)
- Efficient Private Matching and Set Intersection (981 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Differential privacy, Theoretical computer science, Discrete mathematics, Function and Combinatorics.
His Differential privacy study is concerned with Data mining in general.
His biological study deals with issues like Information privacy, which deal with fields such as Confidentiality and Database.
His studies deal with areas such as Computation and Protocol as well as Theoretical computer science.
His Discrete mathematics research incorporates elements of Cluster analysis and Cryptography.
His Algorithm study frequently links to adjacent areas such as Row.
He most often published in these fields:
- Differential privacy (39.87%)
- Theoretical computer science (39.87%)
- Discrete mathematics (19.61%)
What were the highlights of his more recent work (between 2018-2021)?
- Differential privacy (39.87%)
- Discrete mathematics (19.61%)
- Theoretical computer science (39.87%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Differential privacy, Discrete mathematics, Theoretical computer science, Range and Shuffling.
His studies in Differential privacy integrate themes in fields like Machine learning, Protocol and Artificial intelligence.
He combines subjects such as Key and Security parameter with his study of Discrete mathematics.
Many of his studies involve connections with topics such as Sample and Theoretical computer science.
His Range research includes themes of Boolean circuit and Probabilistic logic.
His Shuffling research includes elements of Binary logarithm, Reduction and Matching.
Between 2018 and 2021, his most popular works were:
- The Privacy Blanket of the Shuffle Model (49 citations)
- Differentially Private Summation with Multi-Message Shuffling. (18 citations)
- Private Summation in the Multi-Message Shuffle Model (9 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Statistics
- Artificial intelligence
Shuffling, Discrete mathematics, Binary logarithm, Differential privacy and Context are his primary areas of study.
In his works, Kobbi Nissim conducts interdisciplinary research on Discrete mathematics and Omega.
Kobbi Nissim conducts interdisciplinary study in the fields of Binary logarithm and Bounded function through his works.
Among his research on Bounded function, you can see a combination of other fields of science like Reduction, Mean squared error, Protocol, Arithmetic and Log-log plot.
His Differential privacy research integrates issues from Block and Combinatorics.
Kobbi Nissim incorporates a variety of subjects into his writings, including Context, Theoretical computer science, ENCODE, Core and Dependency.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
