Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision | ||
|
class:privacy2022sgrad [2022/02/23 11:44] jimin1708 [Reading List] |
class:privacy2022sgrad [2025/10/13 12:45] (current) |
||
|---|---|---|---|
| Line 4: | Line 4: | ||
| * **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**: Tuesdays, 10am to 1pm | + | * **Time**: Wednesdays, 1pm to 4pm |
| * **Type**: Graduate Seminar | * **Type**: Graduate Seminar | ||
| * **Goal of the class** | * **Goal of the class** | ||
| Line 13: | Line 13: | ||
| * 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 | ||
| ====== Reading List ====== | ====== Reading List ====== | ||
| - | * {{ :undefined:데이터_비식별화를_이용한_빅데이터_통합.pdf |데이터_비식별화를_이용한_빅데이터_통합}} | ||
| * {{ :class:a_globally_optimal_k-anonymity_method_for_the_de-identification_of_health_data.pdf | a_globally_optimal_k-anonymity_method_for_the_de-identification_of_health_data}} | * {{ :class:a_globally_optimal_k-anonymity_method_for_the_de-identification_of_health_data.pdf | a_globally_optimal_k-anonymity_method_for_the_de-identification_of_health_data}} | ||
| + | * 최적의 k-익명화 | ||
| + | * 건강 데이터 Set의 비식별화를 위한 OLA를 3개의 기존 k-익명 알고리즘과 비교 | ||
| * {{ :class:data_anonymization_using_pseudonym_system_to_preserve_data_privacy.pdf |data_anonymization_using_pseudonym_system_to_preserve_data_privacy}} | * {{ :class:data_anonymization_using_pseudonym_system_to_preserve_data_privacy.pdf |data_anonymization_using_pseudonym_system_to_preserve_data_privacy}} | ||
| + | * 데이터 개인 정보 보호를 위해 가명 시스템을 사용한 데이터 익명화 | ||
| + | * 데이터 유출 위험을 해결하기 위한 인증 과정에서 생체 인증 방법 사용 제안 | ||
| + | * 가명 작성 기법 채택으로 데이터베이스 기록을 익명으로 만들어 적절하게 보호하도록 함 | ||
| * {{ :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}} | ||
| Line 76: | Line 35: | ||
| * {{ :class:privacy_preserving_e-health_data_sharing_in_cloud.pdf |privacy_preserving_e-health_data_sharing_in_cloud}} | * {{ :class:privacy_preserving_e-health_data_sharing_in_cloud.pdf |privacy_preserving_e-health_data_sharing_in_cloud}} | ||
| * {{ :class:secure_k-anonymization_over_encrypted.pdf |secure_k-anonymization_over_encrypted}} | * {{ :class:secure_k-anonymization_over_encrypted.pdf |secure_k-anonymization_over_encrypted}} | ||
| - | * {{ :class:가명_데이터_활용연구_기술적_처리방법_및_기업의_활용방향을_중심.pdf |가명_데이터_활용연구_기술적_처리방법_및_기업의_활용방향을_중심}} | + | * {{ :class:가명_데이터_활용연구_기술적_처리방법_및_기업의_활용방향을_중심.pdf |가명_데이터_활용연구_기술적_처리방법_및_기업의_활용방향을_중심으로}} |
| + | * Data Privacy 보호 관점에서의 관리/통제, 3자 제공 관점에서의 관리 필요 | ||
| + | * 우리나라의 명확치 않은 데이터 활용에 대한 법적 근거 등으로 시장 위축 | ||
| + | * 적절한 비식별 조치 기준 및 위험도 판단 기준 수립 준비를 요하는 논문 | ||
| * {{ :class:개인정보_비식별_제도_해외_동향_및_사례.pdf |개인정보_비식별_제도_해외_동향_및_사례}} | * {{ :class:개인정보_비식별_제도_해외_동향_및_사례.pdf |개인정보_비식별_제도_해외_동향_및_사례}} | ||
| - | * {{ :class:개인정보_비식별_조치를_위한_데이터_상황_기반_위험도_측정_방법.pdf |개인정보 비식별 조치를 위한 데이터 상황 기반의 위험도 측정에 관한 새로운 방법 | + | * 해외 각국의 비식별 제도(미국,EU,일본 등)를 비교하고, 우리나라 비식별 제도 현황과 개선점 도출 |
| - | }} | + | * {{ :class:개인정보_비식별_조치를_위한_데이터_상황_기반_위험도_측정_방법.pdf |개인정보 비식별 조치를 위한 데이터 상황 기반의 위험도 측정에 관한 새로운 방법}} |
| - | + | * {{ :class:개인정보_비식별화_역추적을_위한_머신러닝_활용에_대한_연구_제시.pdf |개인정보_비식별화_역추적을_위한_머신러닝_활용에_대한_연구_제시}} | |
| - | + | * {{ :class:경로정보_개인_비식별화_위한_k-익명성_k값_데이터_활용_익명성_관계.pdf |경로정보 개인 비식별화를 위한 K-익명성의 K값에 따른 데이터 활용과 익명성의 상충관계}} | |
| + | * {{ :class:관계형_데이터베이스서_데이터_그룹화_이용한_익명화_처리_기법.pdf |관계형 데이터베이스에서 데이터 그룹화를 이용한 익명화 처리 기법}} | ||
| + | * {{ :class:데이터_3법_시대의_익명화된_데이터_활용에_대한_제언.pdf |데이터_3법_시대의_익명화된_데이터_활용에_대한_제언}} | ||
| + | * {{ :class:데이터_비식별화를_이용한_빅데이터_통합.pdf |데이터_비식별화를_이용한_빅데이터_통합}} | ||
| + | * {{ :class:비식별_처리_데이터에_대한_정보손실_측정방법_조사연구.pdf |비식별_처리_데이터에_대한_정보손실_측정방법_조사연구}} | ||
| + | * {{ :class:빅데이터_개인정보보호_가이드라인_안_의_개선_방향에_관한_연구.pdf |빅데이터_개인정보보호_가이드라인(안)의_개선_방향에_관한_연구}} | ||
| + | * 시민단체가 정보주체 인권 및 프라이버시 침해를 이유로 강력히 반대 | ||
| + | * 현 가이드라인의 한계점 분석 등 빅데이터 안전 활용 관련 제도적 정비 방안 논의 | ||
| + | * {{ :class:빅데이터_활용_및_사업을_위한_비식별화_전략.pdf |빅데이터_활용_및_사업을_위한_비식별화_전략}} | ||
| + | * {{ :class:의료정보의_관리와_비식별화에_관한_법제_과제.pdf |의료정보의_관리와_비식별화에_관한_법제_과제}} | ||
| + | * {{ :class:이동_컴퓨팅_환경에서_사용자의_익명성과_프라이버시_보장_방안.pdf |이동_컴퓨팅_환경에서_사용자의_익명성과_프라이버시_보장_방안}} | ||
| + | * {{ :class:프라이버시_보호_데이터_유용성_향상_서비스_기반_익명화_기법.pdf |프라이버시 보호와 데이터 유용성 향상을 위한 서비스 기반의 안전한 익명화 기법}} | ||
| - | * {{ :class:gmc2017f:07498625.pdf |Analysis of Multi-Hop Probabilistic Forwarding for Vehicular Safety Applications on Highways}} | ||
| - | * {{ :class:gmc2017f:07857781.pdf |A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware}} | ||
| - | * {{ :class:gmc2017f:07866188.pdf |A Secure Communication Protocol with Privacy-Preserving Monitoring and Controllable Linkability for V2G}} | ||
| - | * {{ :class:gmc2017f:07869305_1_.pdf |A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT}} | ||
| - | * {{ :class:gmc2017f:07869418.pdf |Efficient Privacy-Preserving Dual Authentication and Key Agreement Scheme for Secure V2V Communications in an IoV Paradigm}} | ||
| - | * <fc red>(Taken by Zhong)</fc>{{ :class:gmc2017f:1-s2.0-s0957417415006168-main.pdf |Certificate sharing system for secure certificate distribution in mobile environment}} | ||
| - | * {{ :class:gmc2017f:1-s2.0-s108480451500123x-main.pdf |Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing}} | ||
| - | * <fc red>(Taken by Henock)</fc>{{ :class:gmc2017f:1-s2.0-s1574119212000673-main.pdf |The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices}} | ||
| - | * <fc red>(Taken by Hussein)</fc>{{ :class:gmc2017f:1-s2.0-s1574119214001527-main.pdf |Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior}} | ||
| - | * {{ :class:gmc2017f:1-s2.0-s1574119215001510-main.pdf |Secure authentication scheme for IoT and cloud servers}} | ||
| - | * <fc red>(Taken by Zhong)</fc>{{ :class:gmc2017f:appstract_mobicom16.pdf |Appstract: On-The-Fly App Content Semantics With Better Privacy}} | ||
| - | * <fc red>(Taken by Gun)</fc>{{ :class:gmc2017f:mobicom16_yu.pdf |SALVE: Server Authentication with Location VErification}} | ||
| - | * {{ :class:gmc2017f:sztyler2016unsupervised.pdf |Unsupervised Recognition of Interleaved Activities of Daily Living through Ontological and Probabilistic Reasoning}} | ||
| - | * <fc red>(Taken by Abbas)</fc>{{ :class:gmc2017f:quantum-computing-an-ibm-perspective.pdf |Quantum computing: An IBM perspective}} | ||
class/privacy2022sgrad.1645591460.txt.gz · Last modified: 2025/10/13 12:59 (external edit) · [Old revisions]
