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Minjae Kim 0008

dblp:16/418-8 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-5392-9110ORCID · verified

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

Security and privacy · 2 · 2 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
2 papers
Cryptographic protocols and secure computation · 48% Authentication and access control · 21% Security and privacy of machine learning · 16%
Artificial intelligence
1 paper
Efficient and distributed learning · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Authentication and access control
certificate management
0.712023
Certificate Transparency With Enhanced Privacy · IEEE Trans. Dependable Secur. Comput. 2023
Web and mobile security › web PKI
certificate transparency
0.712023
Certificate Transparency With Enhanced Privacy · IEEE Trans. Dependable Secur. Comput. 2023
Security and privacy of machine learning
federated learning security
0.712023
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning · IEEE Trans. Dependable Secur. Comput. 2023
Cryptographic protocols and secure computation
secure aggregation
0.712023
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning · IEEE Trans. Dependable Secur. Comput. 2023
Cryptographic protocols and secure computation
secure multiparty computation
0.712023
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning · IEEE Trans. Dependable Secur. Comput. 2023
Cryptographic protocols and secure computation › secure aggregation
verifiable secure aggregation
0.712023
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning · IEEE Trans. Dependable Secur. Comput. 2023
Machine learning › Efficient and distributed learning › federated learning › federated learning systems
cross-device federated learning
0.212023
VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning · IEEE Trans. Dependable Secur. Comput. 2023

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

pseudorandom generator · 1.3model recovery attack · 1.3shared value tree · 0.7
YearPublicationVenuePosition
2023 VerSA: Verifiable Secure Aggregation for Cross-Device Federated Learning
abstract
In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g.,$98\%$of encrypted local models are recovered within 21 h). Then, we proposeVerSA, a verifiable secure aggregation protocol for cross-device federated learning.VerSAdoes not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency ofVerSAunder diverse datasets, demonstrating thatVerSAis orders of magnitude faster than verification in prior work.
Changhee Hahn, Hodong Kim, Minjae Kim 0008, Junbeom Hur
IEEE Trans. Dependable Secur. Comput.3
2023 Certificate Transparency With Enhanced Privacy
abstract
Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.
Hyunsoo Kwon, Sangtae Lee, Minjae Kim 0008, Changhee Hahn, Junbeom Hur
IEEE Trans. Dependable Secur. Comput.3