Akito Yamamoto
I am a Ph.D. student in Computer Science at the University of Tokyo, advised by Prof. Tetsuo Shibuya.
My research aims are to develop privacy-preserving methods for statistical genomic analysis and medical data sharing and to deepen (not apply or complicate) the theory of differential privacy.
My personal interests mainly lie in the algorithmic (and theoretical) aspects of computer science, not in "useful" topics for the society.
e-mail: a-ymmt [at] ims.u-tokyo.ac.jp
(If you find any issues or mistakes in our papers or would like the latest revised versions of them, please feel free to contact me.)
Education:
2023.3: M.S. in Computer Science, The University of Tokyo
2021.3: B.S. in Information Science, The University of Tokyo
Work Experience:
2023.4 - Present: Research Fellow (JSPS DC1),
Development of Efficient and Practical Privacy-Preserving Methods for Large-Scale Genomic Statistical Analysis.
2023.4 - Present: Research Assistant,
Algorithmic Foundations for Social Advancement (Group B04, Exploration and Development of the Basic Theory of Algorithms), Grant-in-Aid for Transformative Research Areas, MEXT. [webpage]
Publications:
Akito Yamamoto and Tetsuo Shibuya,
Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data. [arxiv] [comments] ☆☆☆☆
Refereed Conference Papers/Presentations:
Akito Yamamoto and Tetsuo Shibuya,
A Joint Permute-and-Flip and Its Enhancement for Large-Scale Genomic Statistical Analysis,
IEEE International Conference on Data Mining Workshops (IEEE ICDMW) (TrustKDD: International Workshop on Trustworthy Knowledge Discovery and Data Mining) 2023, Dec 1-4, pp.217-226. [paper] [comments] ★
Akito Yamamoto and Tetsuo Shibuya,
Privacy-Preserving Publication of GWAS Statistics using Smooth Sensitivity,
Annual International Conference on Privacy, Security & Trust (PST) 2023, Aug 21-23, pp.1-12. [paper] [comments] ★★
Akito Yamamoto and Tetsuo Shibuya,
Privacy-Preserving Genomic Statistical Analysis Under Local Differential Privacy,
Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy (DBSec) 2023, Jul 19-21, pp.40-48. [paper]
Akito Yamamoto, Eizen Kimura, and Tetsuo Shibuya,
(ε, k)-Randomized Anonymization: ε-Differentially Private Data Sharing with k-Anonymity,
International Conference on Health Informatics (HEALTHINF) 2023, Feb 16-18, pp.287-297. [paper] [comments] ★
Akito Yamamoto and Tetsuo Shibuya,
Efficient and Highly Accurate Differentially Private Statistical Genomic Analysis using Discrete Fourier Transform,
IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom) 2022, Dec 9-11, pp.525-532. [paper] [comments] ★★
Akito Yamamoto and Tetsuo Shibuya,
Privacy-Preserving Statistical Analysis of Genomic Data using Compressive Mechanism with Haar Wavelet Transform,
Privacy and Security Workshop at RECOMB 2022, May 24. [webpage] [biorxiv] (Journal ver. → Journal of Computational Biology, 30(2)) ☆
Akito Yamamoto and Tetsuo Shibuya,
Efficient Differentially Private Methods for a Transmission Disequilibrium Test in Genome Wide Association Studies,
Pacific Symposium on Biocomputing (PSB) 2022, Jan 3-7, pp.85-96. [paper] ★
Akito Yamamoto and Tetsuo Shibuya,
Differentially Private Linkage Analysis with TDT --- the case of two affected children per family,
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) 2021, Dec 9-12, pp.765-770. [paper]
Refereed Journal Papers:
Akito Yamamoto and Tetsuo Shibuya,
Privacy-Preserving Statistical Analysis of Genomic Data using Compressive Mechanism with Haar Wavelet Transform,
Journal of Computational Biology, Volume 30, Issue 2, pp.176-188, 2023. [paper] [comments] (Extended version of the same-titled RECOMB 2022 workshop paper.)
Akito Yamamoto and Tetsuo Shibuya,
More practical differentially private publication of key statistics in GWAS,
Bioinformatics Advances, Volume 1, Issue 1, vbab004, 2021. [paper] [comments]
Research Grants:
2023.4 - 2026.3: Grant-in-Aid for JSPS Fellows [link]
Teaching:
Teaching Assistant:
2022.10 - 2023.2: Computational Complexity Theory (Exercise in Information Science II), Dept. of Information Science, School of Science, the University of Tokyo.
Links: