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Wei Wang 0420

dblp:35/7092-420 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0005-4943-3223ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 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.

Network and information security
1 paper
Cryptographic protocols and secure computation · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic protocols and secure computation › private set intersection
multi-party private set intersection
0.812024
Efficient Scalable Multi-Party Private Set Intersection(-Variants) from Bicentric Zero-Sharing · CCS 2024
Cryptographic protocols and secure computation
private set intersection
0.812024
Efficient Scalable Multi-Party Private Set Intersection(-Variants) from Bicentric Zero-Sharing · CCS 2024
Cryptographic protocols and secure computation › private set intersection
private set intersection cardinality
0.812024
Efficient Scalable Multi-Party Private Set Intersection(-Variants) from Bicentric Zero-Sharing · CCS 2024
Cryptographic protocols and secure computation › private set intersection
threshold private set intersection
0.812024
Efficient Scalable Multi-Party Private Set Intersection(-Variants) from Bicentric Zero-Sharing · CCS 2024

Methods — techniques the papers use, named apart from their topics

symmetric-key cryptography · 0.8oblivious key-value store · 0.8bicentric zero-sharing · 0.8
YearPublicationVenuePosition
2024 Efficient Scalable Multi-Party Private Set Intersection(-Variants) from Bicentric Zero-Sharing
abstract
Multi-party private set intersection (MPSI) allows 𝑛(𝑛 ≥ 3) participants, each holding a dataset of size 𝑚, to compute the intersection of their sets without revealing any additional information.We extract a primitive called bicentric zero-sharing, which can reduce MPSI to two-party PSI between two central participants named Pivot and Leader.We introduce an efficient instantiation of bicentric zero-sharing, which involves a round of sharing and reconstruction of an oblivious key-value store (OKVS) object.We then combine this construction with two-party PSI to propose a new efficient scalable MPSI protocol.We also propose protocols for computing MPSI variants based on bicentric zero-sharing, such as multi-party private set intersection cardinality (MPSI-CA) and multi-party threshold private set intersection (MTPSI).Our protocols are mainly based on symmetric-key operations, and the communication complexity of each participant is at most O (𝑛 + 𝑚).The security of our protocols relies on the assumption * The first two authors contribute equally.
Ying Gao 0006, Yuanchao Luo, Longxin Wang, Wei Wang 0420, Mengmeng Zhou
CCS6