Fetching the paper…
Reading the bibliography…
The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation.
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.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
K. Chen and L. Liu, “A random rotation perturbation approach to privacy preserving data classification,” 2005. [Online]. Available: https://corescholar.libraries.wright.edu/knoesis/916/
2005
Earlier work this paper cites.
N. Li, T. Li, and S. Venkatasubramanian, “t-closeness: Privacy beyond k-anonymity and l-diversity,” in Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on . IEEE, 2007, pp. 106–115
2007
Earlier work this paper cites.
L. Zhang, S. Jajodia, and A. Brodsky, “Information disclosure under realistic assumptions: Privacy versus optimality,” in Proceedings of the 14th ACM conference on Computer and communications security . ACM, 2007, pp. 573–583
2007
Earlier work this paper cites.
X. Xiao and Y. Tao, “Output perturbation with query relaxation,” Proceedings of the VLDB Endowment , vol. 1, no. 1, pp. 857–869, 2008
2008
Earlier work this paper cites.
S. R. Ganta, S. P. Kasiviswanathan, and A. Smith, “Composition attacks and auxiliary information in data privacy,” in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2008, pp. 265–273
2008
Earlier work this paper cites.
C. Dwork, “The differential privacy frontier,” in Theory of Cryptography Conference . Springer, 2009, pp. 496–502
2009
Earlier work this paper cites.
F. D. McSherry, “Privacy integrated queries: an extensible platform for privacy-preserving data analysis,” in Proceedings of the 2009 ACM SIGMOD International Conference on Management of data . ACM, 2009, pp. 19–30
2009
Earlier work this paper cites.
——, “Geometric data perturbation for privacy preserving outsourced data mining,” Knowledge and Information Systems , vol. 29, no. 3, pp. 657–695, 2011
2011
Earlier work this paper cites.
R. C.-W. Wong, A. W.-C. Fu, K. Wang, P. S. Yu, and J. Pei, “Can the utility of anonymized data be used for privacy breaches?” ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 5, no. 3, p. 16, 2011
2011
Earlier work this paper cites.
N. Mohammed, R. Chen, B. Fung, and P. S. Yu, “Differentially private data release for data mining,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2011, pp. 493–501
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” Journal of machine learning research , vol. 12, no. Oct, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein, “Distributed graphlab: a framework for machine learning and data mining in the cloud,” Proceedings of the VLDB Endowment , vol. 5, no. 8, pp. 716–727, 2012
2012
Earlier work this paper cites.
T.-H. H. Chan, M. Li, E. Shi, and W. Xu, “Differentially private continual monitoring of heavy hitters from distributed streams,” in International Symposium on Privacy Enhancing Technologies Symposium . Springer, 2012, pp. 140–159
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
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.
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
Cited alongside, same era.
2014
Cited alongside, same era.
L. M. Vaquero and L. Rodero-Merino, “Finding your way in the fog: Towards a comprehensive definition of fog computing,” ACM SIGCOMM Computer Communication Review , vol. 44, no. 5, pp. 27–32, 2014
2014
Cited alongside, same era.
K. Gai, M. Qiu, H. Zhao, and J. Xiong, “Privacy-aware adaptive data encryption strategy of big data in cloud computing,” in Cyber Security and Cloud Computing (CSCloud), 2016 IEEE 3rd International Conference on . IEEE, 2016, pp. 273–278
2016
Later among the works it cites.
2016
Later among the works it cites.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining . ACM, 2016, pp. 785–794
2016
Later among the works it cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in Security and Privacy (SP), 2017 IEEE Symposium on . IEEE, 2017, pp. 3–18
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
H. Xu, S. Guo, and K. Chen, “Building confidential and efficient query services in the cloud with rasp data perturbation,” IEEE transactions on knowledge and data engineering , vol. 26, no. 2, pp. 322–335, 2014
2014
Cited alongside, same era.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . ACM, 2015, pp. 1322–1333
2015
Cited alongside, same era.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security . ACM, 2015, pp. 1310–1321
2015
Cited alongside, same era.
J. A. Fox, Randomized response and related methods: Surveying Sensitive Data . SAGE Publications, 2015, vol. 58
2015
Cited alongside, same era.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks , vol. 61, pp. 85–117, 2015
2015
Cited alongside, same era.
F. Chollet et al. , “Keras: Deep learning library for theano and tensorflow,” URL: https://keras. io/k , vol. 7, no. 8, 2015
2015
Cited alongside, same era.
A. Machanavajjhala and D. Kifer, “Designing statistical privacy for your data,” Communications of the ACM , vol. 58, no. 3, pp. 58–67, 2015
2015
Cited alongside, same era.
R. Bassily and A. Smith, “Local, private, efficient protocols for succinct histograms,” in Proceedings of the forty-seventh annual ACM symposium on Theory of computing . ACM, 2015, pp. 127–135
2015
Cited alongside, same era.
C. Song, T. Ristenpart, and V. Shmatikov, “Machine learning models that remember too much,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 587–601
2017
Later among the works it cites.
T. Wang, J. Blocki, N. Li, and S. Jha, “Locally differentially private protocols for frequency estimation,” in 26th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 17) , 2017, pp. 729–745
2017
Later among the works it cites.
K. Yang, Q. Han, H. Li, K. Zheng, Z. Su, and X. Shen, “An efficient and fine-grained big data access control scheme with privacy-preserving policy,” IEEE Internet of Things Journal , vol. 4, no. 2, pp. 563–571, 2017
2017
Later among the works it cites.
D. Vatsalan, Z. Sehili, P. Christen, and E. Rahm, “Privacy-preserving record linkage for big data: Current approaches and research challenges,” in Handbook of Big Data Technologies . Springer, 2017, pp. 851–895
2017
Later among the works it cites.
F. Kerschbaum and M. Härterich, “Searchable encryption to reduce encryption degradation in adjustably encrypted databases,” in IFIP Annual Conference on Data and Applications Security and Privacy . Springer, 2017, pp. 325–336
2017
Later among the works it cites.
P. Li, J. Li, Z. Huang, T. Li, C.-Z. Gao, S.-M. Yiu, and K. Chen, “Multi-key privacy-preserving deep learning in cloud computing,” Future Generation Computer Systems , vol. 74, pp. 76–85, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
Later among the works it cites.
M. A. P. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, “Efficient data perturbation for privacy preserving and accurate data stream mining,” Pervasive and Mobile Computing , 2018
2018
Later among the works it cites.
N. Johnson, J. P. Near, and D. Song, “Towards practical differential privacy for sql queries,” Proceedings of the VLDB Endowment , vol. 11, no. 5, pp. 526–539, 2018
2018
Later among the works it cites.
M. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, “An efficient and scalable privacy preserving algorithm for big data and data streams,” Computers & Security , vol. 87, p. 101570, 2019
2019
Closest in time.
2019
Closest in time.
M. A. P. Chamikara, P. Bertok, D. Liu, S. Camtepe, and I. Khalil, “Efficient privacy preservation of big data for accurate data mining,” Information Sciences , 2019
2019
Closest in time.