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class:privacy2022sgrad [2022/02/25 12:48]
jimin1708 [Reading List]
class:privacy2022sgrad [2025/10/13 12:45] (current)
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   * **Provided by**: Dept. of Computer Engineering,​ Myongji University   * **Provided by**: Dept. of Computer Engineering,​ Myongji University
   * **Lead by**: Minho Shin (mhshin@mju.ac.kr,​ Rm5742)   * **Lead by**: Minho Shin (mhshin@mju.ac.kr,​ Rm5742)
-  * **Period**: ​Fall semester, ​2017+  * **Period**: ​Spring ​semester, ​2022
   * **Location**:​ 5701 at 5th Engineering Building   * **Location**:​ 5701 at 5th Engineering Building
-  * **Time**: ​Tuesdays10am to 1pm+  * **Time**: ​Wednesdays, 1pm to 4pm
   * **Type**: Graduate Seminar   * **Type**: Graduate Seminar
   * **Goal of the class**   * **Goal of the class**
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   * Resources ​   * Resources ​
  
-**Participants** 
- 
-^ # ^ Name ^ Dept ^ Advisor ^ Mobile Phone ^ Email Address ^ 
-| 1 | ZHANG ZHONG | CE | Minho Shin | 010-2676-8912 ​ | zhangzhong219017@hotmail.com ​ | 
-| 2 | Gun Noh | CE | Jonghoon Chun | 010-4544-8169 | laylow861@gmail.com | 
-| 3 | Sujeong Lee | CE | Minho Shin | 010-3114-5814 | sujunglee223@gmail.com | 
-| 4 | Yujin Kwon | CE | Seungchul Han |010-2386-5092 | yujin2382@gmail.com | 
-| 5 | Minhae Jang | CE | Yeonseung Ryu | 010-8851-9207 | jully6363@naver.com | 
  
 ====== Agenda ====== ====== Agenda ======
  
-^ Date ^ Name ^  Topic  ^ Slides ^ Data ^ Method ^ Evaluation ^ Contribution  +
-| 2017.*.* | Minho | Topics in Data Privacy |{{ :​class:​gpriv2017f:​lecture-privacy.pptx |}} \\ {{ :​class:​gpriv2017f:​differentialprivacy2.pptx |}} |{{ :​class:​gpriv2017f:​data_new.zip |}} | | |+
  
 ====== Term Project ====== ====== Term Project ======
- 
-  * Goal: Analyze the privacy levels of data using ARX + alpha 
-  * Steps 
-    - Choose your data (already done) 
-      - 권유진: adult 
-      - Zhong: atus 
-      - 장민해: cup 
-      - 이수정: fars 
-      - 노건: ihis 
-    - Download your data and VHG  
-    - Open the data in ARX 
-    - Choose quasi-identifiers 
-    - Choose one sensitive attribute 
-    - Import VHGs 
-    - Check if VHG is valid. If not, fix it. 
-      - Generalization steps should be in detail 
-    - Run de-identification algorithms 
-      - run k-anonymity with k=3, 5, 10 
-        - choose suppression limit as you wish 
-      - run distinctive l-diversity with l=3, 5, 10 
-      - run entropy l-diversity with l=3, 5, 10 
-      - run t-closeness with your choice of t 
-    - For each de-identified data do 
-      - Compute Min/Max/Avg EQ group sizes 
-      - Compute Min/Max/Avg # of different sensitive values ​ 
-      - Compute Min/Max/Avg entropy ​ 
-      - Compute GenInfoLoss 
-      - Compute the privacy levels 
-        - Compute identity-disclosure level 
-          *  {{:​class:​gpriv2017f:​screen_shot_2017-12-06_at_3.18.25_am.png?​150|}} 
-        - Compute attribute-disclosure level 
-          * {{:​class:​gpriv2017f:​screen_shot_2017-12-06_at_3.22.43_am.png?​200|}} 
-        - Compute inference-disclosure level 
-          * {{:​class:​gpriv2017f:​screen_shot_2017-12-06_at_3.23.49_am.png?​200|}} ​ 
-        - Analyze the results of privacy levels 
-     - Discuss the results 
  
  
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     * 데이터 유출 위험을 해결하기 위한 인증 과정에서 생체 인증 방법 사용 제안     * 데이터 유출 위험을 해결하기 위한 인증 과정에서 생체 인증 방법 사용 제안
     * 가명 작성 기법 채택으로 데이터베이스 기록을 익명으로 만들어 적절하게 보호하도록 함     * 가명 작성 기법 채택으로 데이터베이스 기록을 익명으로 만들어 적절하게 보호하도록 함
-    * (이거 버릴까요?​ ㅠㅠ 어찌할까요?​ 가명 작성 기법은 볼 만한 내용인 것 같아서 추가했습니다만...) 
   * {{ :​class:​efficient_systematic_clustering_method_for_k-anonymization.pdf |efficient_systematic_clustering_method_for_k-anonymization}}   * {{ :​class:​efficient_systematic_clustering_method_for_k-anonymization.pdf |efficient_systematic_clustering_method_for_k-anonymization}}
   * {{ :​class:​implementation_and_evaluation_of_an_efficient_secure_computation_system_using_r_for_healthcare_statistics.pdf |implementation_and_evaluation_of_an_efficient_secure_computation_system_using_r_for_healthcare_statistics}}   * {{ :​class:​implementation_and_evaluation_of_an_efficient_secure_computation_system_using_r_for_healthcare_statistics.pdf |implementation_and_evaluation_of_an_efficient_secure_computation_system_using_r_for_healthcare_statistics}}
 
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