최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기情報保護學會誌 = KIISC review, v.28 no.2, 2018년, pp.61 - 77
정강수 (서강대학교 컴퓨터공학과 데이터베이스 연구실) , 박석 (서강대학교 컴퓨터공학과 데이터베이스 연구실)
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
개인정보가 포함된 대용량 데이터 분석이 잠재적인 개인정보 노출의 위험성을 야기했던 사례는 무엇인가? | 그러나 개인정보가 포함된 대용량 데이터 분석은 잠재적인 개인정보노출 위험을 야기한다. 선거인명부를 사용한 매사추세츠 주지사의 병원 기록 정보 노출이나 AOL 검색기록을 통한 특정인 식별, 넷플릭스 평점 자료를 통한 이용자 식별 등의 일련의 프라이버시 침해 사건들은 이와 같은 개인정보노출의 위험성을 보여주는 사례들이다. 개인들은 서비스의 혜택을 받기 위해 개인 데이터를 제공하지만 자신이 허락한 수준 이상의 정보가 드러나는 것은 원하지 않으므로 사용자가 원하는 수준의 프라이버시 보호를 제공하는 것은 데이터의 활용을 위해서도 중요한 목표이다. | |
히스토그램 생성은 무엇인가? | 히스토그램 생성은 비상호적 방식으로 데이터를 배포할 때의 가장 기본적인 방법이다. 그러나 히스토그램을 통한 질의는 노이즈의 총 생성량이 속성의 조합에 비례하여 증가하므로 전체 레코드의 수가 속성의 조합의 수에 비해 작을 때 질의의 유용성이 크게 저하된다. | |
데이터의 활용에 있어서 개인정보노출의 위험을 줄이기 위한 중요한 목표는 무엇인가? | 선거인명부를 사용한 매사추세츠 주지사의 병원 기록 정보 노출이나 AOL 검색기록을 통한 특정인 식별, 넷플릭스 평점 자료를 통한 이용자 식별 등의 일련의 프라이버시 침해 사건들은 이와 같은 개인정보노출의 위험성을 보여주는 사례들이다. 개인들은 서비스의 혜택을 받기 위해 개인 데이터를 제공하지만 자신이 허락한 수준 이상의 정보가 드러나는 것은 원하지 않으므로 사용자가 원하는 수준의 프라이버시 보호를 제공하는 것은 데이터의 활용을 위해서도 중요한 목표이다. |
K. Nissim, S. Raskhodnikova, and A. Smith, "Smooth sensitivity and sampling in private data analysis," in Proceedings of the 39th Annual ACM Symposium on Theory of Computing, San Diego, CA, 2007, pp. 75-84.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, "Our data, ourselves: privacy via distributed noise generation," in Proceedings of the 24th Annual International Conference on The Theory and Applications of Cryptographic Techniques, Saint Petersburg, Russia, 2006, pp. 486-503
F. McSherry and K. Talwar, "Mechanism design via differential privacy," in Proceedings of the 48th Annual IEEE Journal of Computing Science and Engineering, Vol. 7, No. 3, September 2013, pp. 177-186
A. Ghosh, T. Roughgarden, and M. Sundararajan, "Universally utility-maximizing privacy mechanisms," in Proceedings of the 41st Annual ACM Symposium on Theory of Computing, Bethesda, MD, 2009, pp. 351-360.
Chaudhuri, Kamalika, Claire Monteleoni, and Anand D. Sarwate. "Differentially private empirical risk minimization." Journal of Machine Learning Research 12.Mar (2011): 1069-1109.
R. Sarathy and K. Muralidhar, "Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data," Transactions on Data Privacy, vol. 4, no. 1, pp. 1-17, 2011.
K. Muralidhar and R. Sarathy, "Does Differential Privacy Protect Terry Gross' Privacy?," in Privacy in Statistical Databases, vol. 6344, J. Domingo? Ferrer and E. Magkos, Eds. Springer Berlin / Heidelberg, 2011, pp. 200-209
Bambauer, J. R., Muralidhar, K., & Sarathy, R. (2013). Fool's gold: an illustrated critique of differential privacy.
Frank mcsherry, https://github.com/frankmcsherry/blog/blob/master/posts/2016-02-03.md
A. Haeberlen, B. C. Pierce, and A. Narayan, "Differential privacy under fire," in Proceedings of the 20th USENIX Conference on Security, San Francisco, CA, 2011
P. Mohan, A. Thakurta, E. Shi, D. Song, and D. Culler, "GUPT: Privacy preserving data analysis made easy," in Proc. 2012 ACM SIGMOD Int. Conf. Management Data, pp. 349-360.
D. Kifer and A. Machanavajjhala, "No free lunch in data privacy," in Proceedings of the 2011 international conference on Management of data, 2011, pp. 193-204
Dinur I, Nissim K. Revealing information while preserving privacy. In Proceedings of the Twenty-Second ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2003, 202-210.
C. Dwork and S. Yekhanin, "New efficient attacks on statistical disclosure control mechanisms," in Proceedings of the 28th Annual Conference on Cryptology: Advances in Cryptology, Santa Barbara, CA, 2008, pp. 469-480.
A. Blum, K. Ligett, and A. Roth, "A learning theory approach to non-interactive database privacy," in Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, BC, 2008, pp. 609-618
A. Roth and T. Roughgarden, "Interactive privacy via the median mechanism," in Proceedings of the 42nd ACM symposium on Theory of computing, New York, NY, USA, 2010, pp. 765-774
M. Hardt and G. N. Rothblum, "A multiplicative weights mechanism for privacy-preserving data analysis," in Proceedings of the IEEE 51st Annual Symposium on Foundations of Computer Science, Las Vegas, NV, 2010, pp. 61-70
B. Barak, K. Chaudhuri, C. Dwork, S. Kale, F. McSherry, and K. Talwar, "Privacy, accuracy, and consistency too: a holistic solution to contingency table release," in Proceedings of the twenty?sixth ACM SIGMOD?SIGACT? SIGART symposium on Principles of database systems, New York, NY, USA, 2007, pp. 273-282.
G. Acs, C. Castelluccia, and R. Chen, "Differentially private histogram publishing through lossy compression," in Proceedings of the IEEE 12th International Conference on Data Mining, Brussels, Belgium, 2012.
M. Hay, V. Rastogi, G. Miklau, and D. Suciu, "Boosting the accuracy of differentially private histograms through consistency," Proceedings of the VLDB Endowment, vol. 3, no. 1-2, pp. 1021-1032, 2010.
C. Li, M. Hay, V. Rastogi, G. Miklau, and A. McGregor, "Optimizing linear counting queries under differential privacy," in Proceedings of the 29th ACM SIGMOD-SIGACTSIGART Symposium on Principles of Database Systems, Indianapolis, IN, 2010, pp. 123-134
C. Li and G. Miklau, "An adaptive mechanism for accurate query answering under differential privacy," Proceedings of the VLDB Endowment, vol. 5, no. 6, pp. 514-525, 2012
Chen, R., Fung, B. C. M., and Desai, B. C. Differentially private trajectory data publication. CoRR (2011), ?1?1
Cormode, G., Procopiuc, C., Srivastava, D., Shen, E., and Yu, T. Differentially private spatial decompositions. In Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (Washington, DC, USA, 2012), ICDE '12, IEEE Computer Society, pp. 20-31.
S. Peng, Y. Yang, Z. Zhang, M. Winslett, and Y. Yu, "DPtree: indexing multi-dimensional data under differential privacy," in Proceedings of the ACM SIGMOD International Conference on Management of Data, Scottsdale, AZ, 2012, pp. 864-864.
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett. Functional mechanism: Regression analysis under differential privacy. In International Conference on Very Large Data Bases, pages 1364-1375, 2012
Bonomi, Luca, and Li Xiong. "A two-phase algorithm for mining sequential patterns with differential privacy." Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 2013.
Li, N., Qardaji, W., Su, D., and Cao, J. Privbasis: frequent itemset mining with differential privacy. Proc. VLDB Endow. 5, 11 (July 2012), 1340-1351
Geetha Jagannathan, Krishnan Pillaipakkamnatt, and Rebecca N. Wright. A practical differentially private random decision tree classifier. In International Conference on Data Mining Workshops, pages 114-121, 2009.
Arik Friedman and Assaf Schuster. Data mining with differential privacy. In International Conference on Knowledge Discovery and Data Mining, pages 493-502, 2010.
Cynthia Dwork and Jing Lei. Differential privacy and robust statistics. In ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 371-380, 2009.
dam Smith. Efficient, differentially private point estimators. In Computing Research Repository, 2008.
Jing Lei. Differentially private M-estimators. In Advances in Neural Information Processing Systems, pages 361-369, 2011.
HO, S.-S. AND RUAN, S. 2011. Differential privacy for location pattern mining. In Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS. SPRINGL '11. ACM, New York, NY, USA, 17-24
W. Qardaji, W. Yang and N. Li, "Differentially private grids for geospatial data," 2013 IEEE 29th International Conference on Data Engineering (ICDE), Brisbane, QLD, pp. 757-768., 2013
Jun Zhang, Xiaokui Xiao, and Xing Xie., "PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions.", In Proceedings of the 2016 International Conference on Management of Data (SIGMOD '16). ACM, New York, NY, USA, 155-170., 2016
Rui Chen, Benjamin C.M. Fung, Bipin C. Desai, and Neriah M. Sossou., "Differentially private transit data publication: a case study on the montreal transportation system.", In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '12). ACM, New York, NY, USA, 213-221., 2012
D. Shao, K. Jiang, T. Kister, S. Bressan and K.-L. Tan, "Publishing trajectory with differential privacy: A priori vs. a posteriori sampling mechanisms", In DEXA, pages 357-365, 2013
Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M. Procopiuc, and Divesh Srivastava., "DPT: differentially private trajectory synthesis using hierarchical reference systems.", Proc. VLDB Endow. 8, pp. 1154-1165, 2015
Dwork et al., "Differential privacy in new settings", SODA 2010
Yves-Alexandre de Montjoye, Cesar A. Hidalgo, Michel Verleysen, Vincent D. Blondel, "Unique in the crowd: The privacy bounds of human mobility", Sci. Rep., 3(1376), 2013
Georgios Kellaris, Stavros Papadopoulos, Xiaokui Xiao, and Dimitris Papadias, "Differentially private event sequences over infinite streams.", Proc. VLDB Endow. 7, 1155-1166, 2014
Y. Cao and M. Yoshikawa, "Differentially Private Real-Time Data Release over Infinite Trajectory Streams," 2015 16th IEEE International Conference on Mobile Data Management, Pittsburgh, PA, pp. 68-73., 2015
Georgios Kellaris, Stavros Papadopoulos, Xiaokui Xiao, and Dimitris Papadias, "Differentially private event sequences over infinite streams.", Proc. VLDB Endow. 7, 1155-1166, 2014
C. Task and C. Clifton, "A Guide to Differential Privacy Theory in Social Network Analysis," in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.
E. Shen and T, Yu, "Mining Frequent Graph Patterns with Differential Privacy," in KDD' 13, August 11-14, 2013, Chicago, Illinois, USA.
V. Rastogi, M. Hay, G. Miklau and D. Suciu, "Relationship Privacy: Output Perturbation for Queries with Joins," in PODS' 09, June 29-July 2, 2009, Providence, Rhode Island, USA.
Kifer, Daniel, and Ashwin Machanavajjhala. "Pufferfish: A framework for mathematical privacy definitions." ACM Transactions on Database Systems (TODS) 39.1 (2014): 3
He, Xi, Ashwin Machanavajjhala, and Bolin Ding. "Blowfish privacy: Tuning privacy-utility trade-offs using policies." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 2014.
Zhang, Jun, et al. "Privbayes: Private data release via bayesian networks." Proceedings of the 2014 ACM SIGMOD international conference on Management of data. ACM, 2014
Yang, Bin, Issei Sato, and Hiroshi Nakagawa. "Bayesian differential privacy on correlated data." Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 2015
Chen, Rui, et al. "Correlated network data publication via differential privacy." The VLDB Journal 23.4 (2014): 653-676
Liu, Changchang, Supriyo Chakraborty, and Prateek Mittal. "Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples." NDSS. 2016
F. D. McSherry, "Privacy integrated queries: an extensible platform for privacy-preserving data analysis," in Proceedings of the 35th SIGMOD International Conference on Management of Data, Providence, RI, 2009, pp. 19-30.
I. Roy, S. T. V. Setty, A. Kilzer, V. Shmatikov, and E. Witchel, "Airavat: Security and privacy for mapreduce," in Proc. 7th USENIX Conf. Networked Systems Design and Implementation (NSDI '10), Berkeley, CA.
Machanavajjhala, A., Kifer, D., Abowd, J. M., Gehrke, J., and Vilhuber, L. Privacy: Theory meets practice on the map. In ICDE'08 (2008), pp. 277-286
Li, N., Qardaji, W., Su, D., and Cao, J. Privbasis: frequent itemset mining with differential privacy. Proc. VLDB Endow. 5, 11 (July 2012), 1340-1351
Kotsogiannis, I., Hay, M., Machanavajjhala, A., Miklau, G., & Orr, M. (2017, May). DIAS: Differentially Private Interactive Algorithm Selection using Pythia. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1679-1682). ACM.
Erlingsson, U., Pihur, V., & Korolova, A. (2014, November). Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security (pp. 1054-1067).
Prochlo: Strong Privacy for Analytics in the Crowd
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
J. Lee, C. Clifton, "How Much is Enough? Choosing Epsilon for Differential Privacy" Proceedings of the International Conference on Information Security, pp. 325-340, 2011.
J. Hsu, et al, "Differential Privacy: An Economic Method for Choosing Epsilon", Proceedings of the 27th IEEE Computer Security Foundations Symposium, pp.1-29, 2014.
L. Fleischer, Y. Lyu, C. Science, D. College, "Approximately Optimal Auctions for Selling Privacy when Costs are Correlated with Data" Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 568-585, 2012.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.