Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2024 328977 Privacy-Preserving and Secure Computing 1 3.0 3 ★ Chen, Yu-Chi GCSIE
IEECSG
MPIS
MSIS 20 2 二教103，電資外生、資工所和資安學位學程合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2024	328977	Privacy-Preserving and Secure Computing	1	3.0	3	★	Chen, Yu-Chi	GCSIE IEECSG MPIS MSIS	20	2	二教103，電資外生、資工所和資安學位學程合開

Course Plan：

Instructor Chen, Yu-Chi　Office Hours

Email wycchen@ntut.edu.tw

Last Updated 2024-05-23 12:06:59

Course Objective 實現計算已從傳統地本地端機器獨立完成，進而委託上雲端等服務提供者協力完成。然而隨著資料或應用的授權，相關的計算安全性必須重新被探討。本課程將會從資料的隱密性出發，探討如何使用同態加密法輕鬆完成一個安全的授權計算，進而利用其他輕量化元件(混淆電路、私密分享)來取代同態加密並重新設計授權計算協定。接著會轉往定義資料的隱私性，並介紹k匿名等去識別化技術，最終帶入現今最嚴謹的差分隱私。透過上述之保有隱私與安全方法，將著名的人工智慧與機器學習演算法導入授權計算之中，重構不同情境的新型態安全計算框架。 The implementation of computation has been completed independently from the local machine, and then outsourced to the cloud (or some service providers) to complete it. However, with the delegation of data or applications, accordingly the security of such computation must be revisited. This course will start from the data confidentiality, discuss how to use homomorphic encryption to easily complete a secure outsourced computation, and then use other lightweight components (garbled circuit, secret sharing) to replace homomorphic encryption and redesign the secure protocols. Then it will move to define the privacy of data, introduce de-identification techniques such as k-anonymity, and finally bring the most rigorous notion of differential privacy. Through the above-mentioned methods of preserving privacy and security, artificial intelligence and machine learning algorithms are introduced into the outsourced computation to reconstruct a new type of the framework for different scenarios.

Course Schedule 1. Intro to privacy and security with computation 2. Cryptography basic 3. Homomorphic encryption (HE) I 4. Homomorphic encryption (HE) II 5. Applications of HE 6. Secret sharing (SS) I 7. Secret sharing (SS) II 8. Applications of SS 9. Project progress report 10. Garbled circuit I 11. Garbled circuit II 12. Privacy definition 13. Differential privacy I 14. Differential privacy II 15. Privacy preserving machine learning 16. Advanced topics 17. Final project presentation (online) 18. Final project presentation (online)

Evaluation and grading policy Class participation 10% Quizzes and exercises 40% Projects 50%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Ref: Tutorials on the Foundations of Cryptography: Dedicated to Oded Goldreich by Yehuda Lindell (Ed.)

The access to curricular consultation By email

The course corresponds to the SDGs

Does the course incorporate AI

Note
In the last two weeks, please watch the presentation and submit the evaluation form for each team.

Instructor	Chen, Yu-Chi　Office Hours
Email	wycchen@ntut.edu.tw
Last Updated	2024-05-23 12:06:59
Course Objective	實現計算已從傳統地本地端機器獨立完成，進而委託上雲端等服務提供者協力完成。然而隨著資料或應用的授權，相關的計算安全性必須重新被探討。本課程將會從資料的隱密性出發，探討如何使用同態加密法輕鬆完成一個安全的授權計算，進而利用其他輕量化元件(混淆電路、私密分享)來取代同態加密並重新設計授權計算協定。接著會轉往定義資料的隱私性，並介紹k匿名等去識別化技術，最終帶入現今最嚴謹的差分隱私。透過上述之保有隱私與安全方法，將著名的人工智慧與機器學習演算法導入授權計算之中，重構不同情境的新型態安全計算框架。 The implementation of computation has been completed independently from the local machine, and then outsourced to the cloud (or some service providers) to complete it. However, with the delegation of data or applications, accordingly the security of such computation must be revisited. This course will start from the data confidentiality, discuss how to use homomorphic encryption to easily complete a secure outsourced computation, and then use other lightweight components (garbled circuit, secret sharing) to replace homomorphic encryption and redesign the secure protocols. Then it will move to define the privacy of data, introduce de-identification techniques such as k-anonymity, and finally bring the most rigorous notion of differential privacy. Through the above-mentioned methods of preserving privacy and security, artificial intelligence and machine learning algorithms are introduced into the outsourced computation to reconstruct a new type of the framework for different scenarios.
Course Schedule	1. Intro to privacy and security with computation 2. Cryptography basic 3. Homomorphic encryption (HE) I 4. Homomorphic encryption (HE) II 5. Applications of HE 6. Secret sharing (SS) I 7. Secret sharing (SS) II 8. Applications of SS 9. Project progress report 10. Garbled circuit I 11. Garbled circuit II 12. Privacy definition 13. Differential privacy I 14. Differential privacy II 15. Privacy preserving machine learning 16. Advanced topics 17. Final project presentation (online) 18. Final project presentation (online)
Evaluation and grading policy	Class participation 10% Quizzes and exercises 40% Projects 50%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Ref: Tutorials on the Foundations of Cryptography: Dedicated to Oded Goldreich by Yehuda Lindell (Ed.)
The access to curricular consultation	By email
The course corresponds to the SDGs
Does the course incorporate AI
Note	In the last two weeks, please watch the presentation and submit the evaluation form for each team.