Oscar Reparaz

1.4k citations
20 papers · 196 indexed · h-index 9

Oscar Reparaz

18 papers receiving 187 citations

Peers

Oscar Reparaz
Comparison fields: 5 of 18
  • Hardware and Architecture 80
  • Artificial Intelligence 185
  • Signal Processing 52
  • Computer Vision and Pattern Recognition 67
  • Information Systems 23
Replace Pascal Sasdrich with:
Pascal Sasdrich Germany
Ryad Benadjila France
Dong‐Guk Han South Korea
Christophe Clavier France
Søren S. Thomsen Denmark
Johann Heyszl Germany
Gaëtan Cassiers Belgium
Oliver Mischke Germany
Begül Bilgin Belgium
Prasanna Ravi Singapore
Oscar Reparaz relative to Pascal Sasdrich Germany Pascal Sasdrich's profile →
Citations per field
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Pascal Sasdrich · 1×
Citations per year

Countries citing papers authored by Oscar Reparaz

Since Specialization
Citations

This map shows the geographic impact of Oscar Reparaz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Oscar Reparaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oscar Reparaz more than expected).

Fields of papers citing papers by Oscar Reparaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Oscar Reparaz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Oscar Reparaz. The network helps show where Oscar Reparaz may publish in the future.

Co-authorship network

The 21 scholars most cited alongside Oscar Reparaz, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Oscar Reparaz Line = papers co-authored together Oscar Reparaz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 201910
2 20198
3 201827
4 201814
5 201812
6 201819
7 201733
8 201715
9
Additively Homomorphic ring-LWE Masking
20167
10 20161
11 201617
12 20165
13 20164
14
A masked ring-LWE implementation
20151
15
Consolidating Masking Schemes
20157
16 20156
17
Generic DPA attacks: curse or blessing?
20141
18
Higher-Order Glitch Resistant Implementation of the PRESENT S-Box
20140
19 20137
20
Selecting Time Samples for Multivariate DPA Attacks
20122

About Oscar Reparaz

Oscar Reparaz is a scholar working on Hardware and Architecture, Signal Processing and Artificial Intelligence, having authored 20 papers that have together received 196 indexed citations. Recurring topics across this work include Cryptographic Implementations and Security (9 papers), Chaos-based Image/Signal Encryption (5 papers), Advanced Malware Detection Techniques (5 papers), Physical Unclonable Functions (PUFs) and Hardware Security (5 papers), Coding theory and cryptography (4 papers), Security and Verification in Computing (3 papers), Cryptography and Data Security (2 papers) and Cryptography and Residue Arithmetic (2 papers). The work is most often cited by research in Hardware and Architecture (80 citations), Artificial Intelligence (185 citations) and Signal Processing (52 citations). Oscar Reparaz has collaborated with scholars based in Belgium, Netherlands and United Kingdom. Frequent co-authors include Ingrid Verbauwhede, Begül Bilgin, Josep Balasch, Fréderik Vercauteren, Sujoy Sinha Roy, Ruan de Clercq, Jo Vliegen, Nele Mentens, Angshuman Karmakar and Hwajeong Seo. Their work appears in journals such as IACR Transactions on Cryptographic Hardware and Embedded Systems, International Journal of Circuit Theory and Applications, Journal of Cryptographic Engineering, IEEE Transactions on Dependable and Secure Computing and IEEE Transactions on Computers.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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