Demo Data from EDBT 2026 / reviewers in the wild / expert
Kabir Sheth
dblp:342/8106
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% | |
| Network and information security
1 paper |
Cryptographic primitives and cryptanalysis · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis › homomorphic encryption
fully homomorphic encryption |
0.7 | 1 | 2023 | Coyote: A Compiler for Vectorizing Encrypted Arithmetic Circuits · ASPLOS (3) 2023 |
Compilers and program optimization › vectorization
SIMD vectorization |
0.7 | 1 | 2023 | Coyote: A Compiler for Vectorizing Encrypted Arithmetic Circuits · ASPLOS (3) 2023 |
Compilers and program optimization
vectorization |
0.7 | 1 | 2023 | Coyote: A Compiler for Vectorizing Encrypted Arithmetic Circuits · ASPLOS (3) 2023 |
Methods — techniques the papers use, named apart from their topics
vectorization · 1.3circuit compilation · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Coyote: A Compiler for Vectorizing Encrypted Arithmetic CircuitsabstractFully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to operate on encrypted data and produce a result that, when decrypted, yields the result of the unencrypted computation. While FHE enables privacy-preserving computation, it is extremely slow. However, the mathematical formulation of FHE supports a SIMD-like execution style, so recent work has turned to vectorization to recover some of the missing performance. Unfortunately, these approaches do not work well for arbitrary computations: they do not account for the high cost of rotating vector operands to allow data to be used in multiple operations. Hence, the cost of rotation can outweigh the benefits of vectorization. Raghav Malik, Kabir Sheth, Milind Kulkarni 0001 |
ASPLOS (3) | 2 |