Demo Data from VLDB 2026 / reviewers in the wild / expert
Miquel Guiot
dblp:373/1506
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0006-3187-2321ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 since 2021Systems, architecture and hardware · 1 · 1 since 2021Theory of computation · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 80% Storage systems · 20% | |
| Network and information security
1 paper |
Cryptographic protocols and secure computation · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic protocols and secure computation › secret sharing › access structure
general access structures |
1.0 | 1 | 2026 | Traceable Secret Sharing Schemes for General Access Structures · EUROCRYPT 2026 |
Cryptographic protocols and secure computation
secret sharing |
1.0 | 1 | 2026 | Traceable Secret Sharing Schemes for General Access Structures · EUROCRYPT 2026 |
Cryptographic protocols and secure computation › secret sharing
traceable secret sharing |
1.0 | 1 | 2026 | Traceable Secret Sharing Schemes for General Access Structures · EUROCRYPT 2026 |
Memory systems
DRAM |
0.8 | 1 | 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute Memory · IEEE Trans. Computers 2024 |
Storage systems
key-value storage |
0.8 | 1 | 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute Memory · IEEE Trans. Computers 2024 |
Memory systems › processing-in-memory
near-memory processing |
0.8 | 1 | 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute Memory · IEEE Trans. Computers 2024 |
Memory systems
processing-in-memory |
0.8 | 1 | 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute Memory · IEEE Trans. Computers 2024 |
Memory systems › processing-in-memory
processing-using-DRAM |
0.8 | 1 | 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute Memory · IEEE Trans. Computers 2024 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Traceable Secret Sharing Schemes for General Access Structures
Oriol Farràs, Miquel Guiot |
EUROCRYPT | 2 |
| 2024 | Reducing the Share Size of Weighted Threshold Secret Sharing Schemes via Chow Parameters Approximation
Oriol Farràs, Miquel Guiot |
TCC (4) | 2 |
| 2024 | $\mathcal{O}(n)$O(n) Key-Value Sort With Active Compute MemoryabstractWe propose the Active Compute Memory (ACM), a near-memory-processing architecture capable of performing key–value sort directly in the DRAM. In the ACM architecture, sort is merely the writing of data into memory with one addressing protocol (perspective) and reading it back with different perspective. The first perspective is conventional, based on the data address; the second perspective is the sorted order. The ACM requires additional tables to store the meta-data and moderate control logic enhancements that can be implemented directly in the DRAM silicon. By these modest enhancements to DRAM, ACM exploits the parallelism inherently available in the row buffer to enable sort with$O(n)$complexity. This leads to an order of magnitude improvement in ACM performance and energy compared to conventional$O(n\log{}n)$CPU-centric sort algorithms. The ACM also shows superior performance compared to other near-memory sort accelerators. This is because the ACM processing is done near the row buffer and it exploits much lower memory access latency, higher bandwidth and wider parallel processing. The sort operation covered in this paper is just an example of an address management operation that can be efficiently implemented directly in the DRAM silicon. We release as an open source the simulation infrastructure for the ACM performance and energy modeling. We would encourage the community to use it, adapt it to other PIM proposals, and share their own evaluations. Pouya Esmaili-Dokht, Miquel Guiot, Petar Radojkovic, Xavier Martorell, Eduard Ayguadé, Jesús Labarta, Jason Adlard, Paolo Amato, Marco Sforzin |
IEEE Trans. Computers | 2 |