Akito Yamamoto (山本章人)

Akito Yamamoto

I am a Ph.D. student in Computer Science at the University of Tokyo, advised by Prof. Tetsuo Shibuya.

My current 'research' aim is to develop privacy-preserving methods for genomic statistical analysis and (medical) data publication through the exploration (≠ "application") of the purest indistinguishability and indistinguishable distributions.

I value amusing aspects of algorithms (and theories) over "useful" topics in the society. My goal in 'research' is to find pieces of what is sufficiently amusing and naturally useful, yet not "rooted in this weird society".

I believe that what is truly amusing must be useful in any society. I believe also that what is "useful only in a specific society" will be completely boring. (The same goes for "good science" and "well-regarded research".)

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.)

lab: Shibuya Lab

cf. 裏

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:

2025.4 - Present: Research Assistant, The University of Tokyo,

Big Data Processing with Compressed Secure Computation, Grant-in-Aid for Scientific Research (S), JSPS. [link]

2023.4 - Present: Research Fellow (JSPS DC1),

Development of Efficient and Practical Privacy-Preserving Methods for Large-Scale Genomic Statistical Analysis.

2023.4 - 2025.3: Research Assistant, The University of Tokyo,

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:

Refereed Conference Papers/Presentations:

Akito Yamamoto and Tetsuo Shibuya,

Direction-Oriented Smooth Sensitivity and Its Application to Genomic Statistical Analysis,

Australasian Conference on Information Security and Privacy (ACISP) 2025, Jul 14-16, to appear. ★

Akito Yamamoto and Tetsuo Shibuya,

Differentially Private Selection using Smooth Sensitivity,

IEEE International Performance, Computing and Communications Conference (IEEE IPCCC) 2024, Nov 22-24, pp.1-10. [paper] [comments] ★★★

Akito Yamamoto and Tetsuo Shibuya,

Generalization and Enhancement of Piecewise Mechanism for Collecting Multidimensional Data,

IEEE International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage (IEEE SpaCCS) 2024, Oct 30-Nov 2, in press. ★★

Akito Yamamoto and Tetsuo Shibuya,

Privacy-Optimized Randomized Response for Sharing Multi-Attribute Data,

IEEE Symposium on Computers and Communications (IEEE ISCC) 2024, Jun 26-29, pp.1-8. [paper] [comments] ★★

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]

Grants:

2023.4 - 2026.3: Grant-in-Aid for JSPS Fellows [link]
2022.1: PSB 2022 Travel Award

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.

Misc:

School of Science, the University of Tokyo “The Rigakubu News” Vol. 55, Issue 6, Mar 2024, p.14. (In Japanese)
IMSUT “PLATINUM STREET TIMES” Vol. 5, Dec 2022, p.3. (In Japanese) [pdf]