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Local Differential Privacy (LDP) is popularly used in practice for privacy-preserving data collection.
S. L. Warner, “Randomized response: A survey technique for eliminating evasive answer bias,” Journal of the American Statistical Association , vol. 60, no. 309, pp. 63–69, 1965
1965
Earlier work this paper cites.
C. Dwork, “Differential privacy,” in International Colloquium on Automata, Languages, and Programming . Springer, 2006, pp. 1–12
2006
Earlier work this paper cites.
F. McSherry and K. Talwar, “Mechanism design via differential privacy,” in 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS) . IEEE, 2007, pp. 94–103
2007
Earlier work this paper cites.
M. Hay, C. Li, G. Miklau, and D. Jensen, “Accurate estimation of the degree distribution of private networks,” in 9th IEEE International Conference on Data Mining (ICDM) . IEEE, 2009, pp. 169–178
2009
Earlier work this paper cites.
S. B. Mehdi, A. K. Tanwani, and M. Farooq, “Imad: in-execution malware analysis and detection,” in Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation . ACM, 2009, pp. 1553–1560
2009
Earlier work this paper cites.
D. H. Chau, C. Nachenberg, J. Wilhelm, A. Wright, and C. Faloutsos, “Polonium: Tera-scale graph mining and inference for malware detection,” in Proceedings of the 2011 SIAM International Conference on Data Mining . SIAM, 2011, pp. 131–142
2011
Earlier work this paper cites.
N. Karampatziakis, J. W. Stokes, A. Thomas, and M. Marinescu, “Using file relationships in malware classification,” in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment . Springer, 2012, pp. 1–20
2012
Earlier work this paper cites.
M. Egele, T. Scholte, E. Kirda, and C. Kruegel, “A survey on automated dynamic malware-analysis techniques and tools,” ACM Computing Surveys (CSUR) , vol. 44, no. 2, p. 6, 2012
2012
Earlier work this paper cites.
D. Sánchez, M. Batet, D. Isern, and A. Valls, “Ontology-based semantic similarity: A new feature-based approach,” Expert Systems with Applications , vol. 39, no. 9, pp. 7718–7728, 2012
2012
Earlier work this paper cites.
D. Chen, S. L. Sain, and K. Guo, “Data mining for the online retail industry: A case study of rfm model-based customer segmentation using data mining,” Journal of Database Marketing & Customer Strategy Management , vol. 19, no. 3, pp. 197–208, 2012
2012
Earlier work this paper cites.
J. C. Duchi, M. I. Jordan, and M. J. Wainwright, “Local privacy and statistical minimax rates,” in 2013 IEEE 54th Annual Symposium on Foundations of Computer Science (FOCS) . IEEE, 2013, pp. 429–438
2013
Earlier work this paper cites.
N. Li, W. Qardaji, D. Su, Y. Wu, and W. Yang, “Membership privacy: a unifying framework for privacy definitions,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & Communications Security . ACM, 2013, pp. 889–900
2013
Earlier work this paper cites.
K. Chatzikokolakis, M. E. Andres, N. E. Bordenabe, and C. Palamidessi, “Broadening the scope of differential privacy using metrics,” in International Symposium on Privacy Enhancing Technologies (PETS) . Springer, 2013, pp. 82–102
2013
Earlier work this paper cites.
M. Andres, N. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geo-indistinguishability: Differential privacy for location-based systems,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2013, pp. 901–914
2013
Earlier work this paper cites.
Ú. Erlingsson, V. Pihur, and A. Korolova, “Rappor: Randomized aggregatable privacy-preserving ordinal response,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2014, pp. 1054–1067
2014
Earlier work this paper cites.
A. Tamersoy, K. Roundy, and D. H. Chau, “Guilt by association: large scale malware detection by mining file-relation graphs,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2014, pp. 1524–1533
2014
Cited alongside, same era.
S. P. Kasiviswanathan and A. Smith, “On the semantics of differential privacy: A bayesian formulation,” Journal of Privacy and Confidentiality , vol. 6, no. 1, 2014
2014
Cited alongside, same era.
N. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Optimal geo-indistinguishable mechanisms for location privacy,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2014, pp. 251–262
2014
Cited alongside, same era.
J. Soria-Comas, J. Domingo-Ferrer, D. Sánchez, and S. Martínez, “Enhancing data utility in differential privacy via microaggregation-based k-anonymity,” The VLDB Journal – The International Journal on Very Large Data Bases , vol. 23, no. 5, pp. 771–794, 2014
A. Inan, M. E. Gursoy, and Y. Saygin, “Sensitivity analysis for non-interactive differential privacy: bounds and efficient algorithms,” IEEE Transactions on Dependable and Secure Computing , 2017
2017
Later among the works it cites.
R. Bassily, U. Stemmer, A. G. Thakurta et al. , “Practical locally private heavy hitters,” in Advances in Neural Information Processing Systems , 2017, pp. 2288–2296
2017
Later among the works it cites.
P. Kairouz, S. Oh, and P. Viswanath, “The composition theorem for differential privacy,” IEEE Transactions on Information Theory , vol. 63, no. 6, pp. 4037–4049, 2017
2017
Later among the works it cites.
A. Smith, A. Thakurta, and J. Upadhyay, “Is interaction necessary for distributed private learning?” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 58–77
2017
Later among the works it cites.
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2014
Cited alongside, same era.
P. Kairouz, S. Oh, and P. Viswanath, “Extremal mechanisms for local differential privacy,” in Advances in Neural Information Processing Systems , 2014, pp. 2879–2887
2014
Cited alongside, same era.
B. Yang, I. Sato, and H. Nakagawa, “Bayesian differential privacy on correlated data,” in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data . ACM, 2015, pp. 747–762
2015
Cited alongside, same era.
R. Bassily and A. Smith, “Local, private, efficient protocols for succinct histograms,” in Proceedings of the 47th Annual ACM Symposium on Theory of Computing . ACM, 2015, pp. 127–135
2015
Cited alongside, same era.
G. Fanti, V. Pihur, and Ú. Erlingsson, “Building a rappor with the unknown: Privacy-preserving learning of associations and data dictionaries,” Proceedings on Privacy Enhancing Technologies , vol. 2016, no. 3, pp. 41–61, 2016
2016
Cited alongside, same era.
Z. Qin, Y. Yang, T. Yu, I. Khalil, X. Xiao, and K. Ren, “Heavy hitter estimation over set-valued data with local differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 192–203
2016
Cited alongside, same era.
R. Chen, H. Li, A. Qin, S. P. Kasiviswanathan, and H. Jin, “Private spatial data aggregation in the local setting,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE) . IEEE, 2016, pp. 289–300
2016
Cited alongside, same era.
T. Wang, J. Blocki, N. Li, and S. Jha, “Locally differentially private protocols for frequency estimation,” in Proc. of the 26th USENIX Security Symposium , 2017, pp. 729–745
2017
Cited alongside, same era.
A. G. Thakurta, A. H. Vyrros, U. S. Vaishampayan, G. Kapoor, J. Freudiger, V. R. Sridhar, and D. Davidson, “Learning new words,” Mar. 14 2017, uS Patent 9,594,741
2017
Cited alongside, same era.
Z. Qin, T. Yu, Y. Yang, I. Khalil, X. Xiao, and K. Ren, “Generating synthetic decentralized social graphs with local differential privacy,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 425–438
2017
Later among the works it cites.
B. Avent, A. Korolova, D. Zeber, T. Hovden, and B. Livshits, “BLENDER: Enabling local search with a hybrid differential privacy model,” in 26th USENIX Security Symposium (USENIX Security 17) . Vancouver, BC: USENIX Association, 2017, pp. 747–764
2017
Later among the works it cites.
Z. Ding, Y. Wang, G. Wang, D. Zhang, and D. Kifer, “Detecting violations of differential privacy,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2018, pp. 475–489
2018
Later among the works it cites.
T. Wang, N. Li, and S. Jha, “Locally differentially private frequent itemset mining,” in IEEE Symposium on Security and Privacy (SP) . IEEE, 2018
2018
Later among the works it cites.
“Pos dataset,” https://github.com/cpearce/HARM/blob/master/datasets/BMS-POS.csv
2018
Later among the works it cites.
M. Bun, J. Nelson, and U. Stemmer, “Heavy hitters and the structure of local privacy,” in Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems . ACM, 2018, pp. 435–447
2018
Later among the works it cites.
G. Cormode, T. Kulkarni, and D. Srivastava, “Marginal release under local differential privacy,” in Proceedings of the 2018 International Conference on Management of Data . ACM, 2018, pp. 131–146
2018
Later among the works it cites.
Z. Zhang, T. Wang, N. Li, S. He, and J. Chen, “Calm: Consistent adaptive local marginal for marginal release under local differential privacy,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2018, pp. 212–229
2018
Later among the works it cites.
N. Wang, X. Xiao, Y. Yang, T. D. Hoang, H. Shin, J. Shin, and G. Yu, “Privtrie: Effective frequent term discovery under local differential privacy,” in IEEE International Conference on Data Engineering (ICDE) , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
C. Liu, X. He, T. Chanyaswad, S. Wang, and P. Mittal, “Investigating statistical privacy frameworks from the perspective of hypothesis testing,” Proceedings on Privacy Enhancing Technologies , vol. 2019, no. 3, pp. 233–254, 2019
2019
Closest in time.