What is he best known for?
The fields of study he is best known for:
- Algorithm
- Algebra
- Cryptography
Emmanuel Prouff spends much of his time researching Side channel attack, Algorithm, Block cipher, Power analysis and Theoretical computer science.
His Side channel attack study is concerned with the larger field of Cryptography.
Algorithm and Masking are two areas of study in which Emmanuel Prouff engages in interdisciplinary research.
His study explores the link between Block cipher and topics such as Security parameter that cross with problems in Masking, Computer engineering, Countermeasure, Order and Multiplication.
The various areas that Emmanuel Prouff examines in his Power analysis study include Adversary, Mutual information and Data mining.
Emmanuel Prouff has included themes like Deep learning, Artificial intelligence and Computation in his Theoretical computer science study.
His most cited work include:
- Provably secure higher-order masking of AES (337 citations)
- Statistical Analysis of Second Order Differential Power Analysis (230 citations)
- Masking against Side-Channel Attacks: A Formal Security Proof (174 citations)
What are the main themes of his work throughout his whole career to date?
Emmanuel Prouff mainly focuses on Side channel attack, Cryptography, Algorithm, Block cipher and Theoretical computer science.
His research in Side channel attack intersects with topics in Exploit, Masking, Computer engineering and Power analysis.
His Cryptography study combines topics in areas such as Function and Hamming distance.
His work on Correlation attack as part of general Algorithm study is frequently linked to Masking, First order and Hamming weight, therefore connecting diverse disciplines of science.
His research integrates issues of Computation, Arithmetic, Security parameter and Implementation in his study of Block cipher.
His biological study deals with issues like Adversary, which deal with fields such as Mathematical proof.
He most often published in these fields:
- Side channel attack (52.59%)
- Cryptography (31.11%)
- Algorithm (28.15%)
What were the highlights of his more recent work (between 2017-2021)?
- Side channel attack (52.59%)
- Artificial intelligence (8.89%)
- Deep learning (5.93%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Side channel attack, Artificial intelligence, Deep learning, Implementation and Randomness.
In his research, he undertakes multidisciplinary study on Side channel attack and Singular spectrum analysis.
His work on Artificial neural network, Preprocessor and Independent component analysis as part of general Artificial intelligence research is often related to Blind signal separation and Signal processing, thus linking different fields of science.
His Deep learning research is multidisciplinary, incorporating elements of Computer security and Information retrieval.
The Implementation study combines topics in areas such as Adversary, Mathematical proof and Theoretical computer science.
His study in Randomness is interdisciplinary in nature, drawing from both Discrete mathematics, Multiplicative function, Computation and Block cipher.
Between 2017 and 2021, his most popular works were:
- Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database. (70 citations)
- Deep Learning to Evaluate Secure RSA Implementations (20 citations)
- A Comprehensive Study of Deep Learning for Side-Channel Analysis (18 citations)
In his most recent research, the most cited papers focused on:
- Algebra
- Algorithm
- Cryptography
Emmanuel Prouff mainly investigates Deep learning, Artificial intelligence, Side channel attack, Context and Information retrieval.
Emmanuel Prouff has included themes like Computer security and Open platform in his Artificial intelligence study.
Computer security is closely attributed to Implementation in his research.
His work carried out in the field of Open platform brings together such families of science as Cryptography, Convolutional neural network and Database.
His Visualization research includes elements of Artificial neural network and Information leakage.
His Upper and lower bounds research includes elements of Mutual information, Soundness, Theoretical computer science and Entropy.
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