I used AI-based technologies to do Data De-identification and Re-identification in the 2022 Attack and Defense Contest hosted by Research Center for Information Technology Innovation, Academia Sinica.

1. CHEN, TSAI-MIN(陳 在民) Ph.D. Candidate
Graduate Program of Data Science, National Taiwan University and
Academia Sinica
Supervisors: Dr. Tsao, Yu(曹 昱) &
Dr. Shen, Chun-Yen(沈 俊嚴)
Data De-identification
and Re-identification
(Attack and Defense Contest)

2. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Jellyfish-like Talent
Entomology
Microbiology Artificial
Intelligence
人工智慧學校
專案處-顧問
中研院生物醫學所
研究助理
日本京大學
分子微生物學所
台灣大學
昆蟲學系
政治大學
政治學系
Information
Security
中研院&台灣大學
資料科學學位學程
Find a way out(O)
Cross-domain?(X)
https://www.linkedin.com/in/%E9%99%B3%E5%9C%A8%E6%B0%91-tsai-min-chen-4675449a/
Political
Science
Medicine

3. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
3
• De-identification is the process used
to prevent someone's personal
identity from being revealed.
• Biomedical data may be de-identified
in order to comply with USA HIPAA
regulations that define and stipulate
patient privacy laws.
What is de-identification for?
Rights (OCR), Office for Civil (2012-09-07). "Methods for De-identification of PHI". HHS.gov. Retrieved 2020-11-08.
Health Insurance Portability and
Accountability Act (HIPAA)

4. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Attack and Defense Contest
(Data De-identification and Re-identification)
Records(3915):
Unique personal ID
17 Features Our decrypting
model
Encrypted Records
A.I.
17
Features
A.I.
Our encrypting
model
:
:
De-identification Re-identification

5. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Evaluation Metrics for De-identification
Convergence in distribution
Perfect Conditions for metrics:
Feature dependency
Individual similarity
Convergence in distribution & Feature dependency
Convergence in distribution
Convergence in distribution
Convergence in distribution
Convergence in distribution & Feature dependency
Convergence in distribution & Feature dependency
Convergence in distribution & Feature dependency

6. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Individual similarity?
Feature dependency
Convergence in distribution
Perfect Conditions for metrics:
Convergence in distribution & Feature dependency
Convergence in distribution
Convergence in distribution
Convergence in distribution
Convergence in distribution & Feature dependency
Convergence in distribution & Feature dependency
Convergence in distribution & Feature dependency
Row Swapping for De-identification
Genetic Algorithm

7. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Encrypting Model by Genetic Algorithm
Best individual similarity

8. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
De-identification Leaderboard
1
Best individual similarity

9. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Evaluation Metrics for Re-identification
• To be simple, just 2 points:
1. Preventing contestants
knowing real personal IDs
of your encrypted data.
2. Deciphering contestants’
real personal IDs of their
encrypted data.

10. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Evaluation Metrics for Re-identification
• To be simple, just 2 points:
1. Preventing contestants
knowing real personal IDs
of your encrypted data.
2. Deciphering contestants’
real personal IDs of their
encrypted data.
Genetic Algorithm is with
randomness naturally.
Deep Learning Algorithm
is strong in decoding.

11. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
De-identification
Decrypting Model
Decrypting Model by Deep Learning
?
?
?
?
?
Re-identification

12. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Re-identification Leaderboard
Ranking Team Name Defense Rate(%) My Hit Rate(%)
1 b99612040 100 0
1 Phil0125 100 0
1 ck99831 100 0
1 zd408 100 0
5 jokerL88 98 0
6 SkyLiNing 86 14
7 Chris7 60 21
8 xiew9222 39 25
9 ken14420 33 52
Possible to decode
Myself
Impossible to decode

13. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Conclusion
I am the Champion!! • To be simple, just 2 points:
1. Randomness is
impossible to decode.
2. Deep Learning is
strong for those
possible to decode.

14. Re-identification
De-identification
Introduction Conclusion
CHEN, TSAI-MIN
Ph.D. Candidate
Question & Answer
Any questions are fine!
一、頂級期刊:
1.IEEE Trans. on Consumer Electronics (minor revision)
2.iScience doi:10.1016/j.isci.2020.100886.
3.Nature Communications doi:10.1038/s41467-021-23165-1.
二、程式競賽:
1.資料去識別化與重新識別攻防競賽 冠軍
2.三總 2022資料科學競賽 冠軍
3.Tomofun 狗音辨識 AI 百萬挑戰賽 2021 第五名
4.IEEE Big Data FEMH Voice Data Challenge 2019 亞軍
5.The China Physiological Signal Challenge 2018 冠軍
6&7.英科智能MolHack 線上黑客松2018 冠軍、2019 季軍
8.第16回數理工學Programming Contest 季軍
三、獎項榮耀:
1.臺大 優秀青年
2.AI智能雲端運算應用競賽 優選
3.NTU-IBM量子計算Qiskit Hackathon Taiwan 季軍
4.臺大 利他獎
5.臺大統計應用論文競賽 優等獎
6.臺大學生代表大會 學生代表
7.量子計算 Quantum Computing 社群發起人
8.AI in TAIWAN台灣人工智慧黑客松 優等獎
9.TMU–MIT醫療數據松 季軍
10.台灣人工智慧技術大賽 亞軍
11.臺大成績優良 兩次
陳在民、1991/03/22出生、男性
…更多請看
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