Fetching the paper…
Reading the bibliography…
For population studies or for the training of complex machine learning models, it is often required to gather data from different actors.
Karp, R.M.: Reducibility among combinatorial problems. In: Complexity of Computer Computations, pp. 85–103 (1972)
1972
Earlier work this paper cites.
Chaum, D.L.: Untraceable electronic mail, return addresses, and digital pseudonyms. Communications of the ACM 24
1981
Earlier work this paper cites.
Santha, M., Vazirani, U.V.: Generating quasi-random sequences from slightly-random sources. In: 25th Annual Symposium on Foundations of Computer Science. pp. 434–440 (1984)
1984
Earlier work this paper cites.
Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation. In: Proceedings of the twentieth annual ACM symposium on Theory of computing. pp. 1–10 (1988)
1988
Earlier work this paper cites.
Chaum, D., Crépeau, C., Damgard, I.: Multiparty unconditionally secure protocols. In: Proceedings of the twentieth annual ACM symposium on Theory of computing. pp. 11–19 (1988)
1988
Earlier work this paper cites.
Impagliazzo, R., Zuckerman, D.: How to recycle random bits. In: 30th Annual Symposium on Foundations of Computer Science. pp. 248–253 (1989)
1989
Earlier work this paper cites.
Coster, M.J., LaMacchia, B.A., Odlyzko, A.M., Schnorr, C.P.: An improved low-density subset sum algorithm. In: Workshop on the Theory and Application of of Cryptographic Techniques. pp. 54–67 (1991)
1991
Earlier work this paper cites.
Galil, Z., Margalit, O.: An almost linear-time algorithm for the dense subset-sum problem. SIAM Journal on Computing 20
1991
Earlier work this paper cites.
Joux, A., Stern, J.: Improving the critical density of the Lagarias-Odlyzko attack against subset sum problems. In: International Symposium on Fundamentals of Computation Theory. pp. 258–264 (1991)
1991
Earlier work this paper cites.
Impagliazzo, R., Naor, M.: Efficient cryptographic schemes provably as secure as subset sum. Journal of cryptology 9
1996
Earlier work this paper cites.
Goldreich, O.: Foundations of Cryptology: Basic Tools. Cambridge (2001)
2001
Earlier work this paper cites.
Syverson, P., Dingledine, R., Mathewson, N.: Tor: The second-generation onion router. In: Usenix Security (2004)
2004
Earlier work this paper cites.
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography Conference. pp. 265–284 (2006)
2006
Earlier work this paper cites.
Kleinberg, J., Tardos, É.: Algorithm Design. Pearson (2006)
2006
Cited alongside, same era.
Fienberg, S.E., Rinaldo, A., Yang, X.: Differential privacy and the risk-utility tradeoff for multi-dimensional contingency tables. In: International Conference on Privacy in Statistical Databases. pp. 187–199 (2010)
2010
Cited alongside, same era.
Becker, A., Coron, J.S., Joux, A.: Improved generic algorithms for hard knapsacks. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques. pp. 364–385 (2011)
2011
Cited alongside, same era.
Beimel, A.: Secret-sharing schemes: a survey. In: International Conference on Coding and Cryptology. pp. 11–46 (2011)
2011
Cited alongside, same era.
Kasiviswanathan, S.P., Lee, H.K., Nissim, K., Raskhodnikova, S., Smith, A.: What can we learn privately? SIAM Journal on Computing 40
2011
Kabanets, V.: Lecture notes in computability & complexity. http://www2.cs.sfu.ca/˜kabanets/308/lectures/lec12.pdf (2016)
2016
Later among the works it cites.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H.B., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. pp. 1175–1191 (2017)
2017
Later among the works it cites.
Emiris, I.Z., Karasoulou, A., Tzovas, C.: Approximating multidimensional subset sum and minkowski decomposition of polygons. Mathematics in Computer Science 11
2017
Later among the works it cites.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-Efficient Learning of Deep Networks from Decentralized Data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. vol. 54, pp. 1273–1282 (2017)
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…
Cited alongside, same era.
Lindell, Y., Oxman, E., Pinkas, B.: The IPS compiler: Optimizations, variants and concrete efficiency. In: Annual Cryptology Conference. pp. 259–276 (2011)
2011
Cited alongside, same era.
Shi, E., Chan, H., Rieffel, E., Chow, R., Song, D.: Privacy-preserving aggregation of time-series data. In: Annual Network & Distributed System Security Symposium (2011)
2011
Cited alongside, same era.
Bernstein, D.J., Jeffery, S., Lange, T., Meurer, A.: Quantum algorithms for the subset-sum problem. In: International Workshop on Post-Quantum Cryptography. pp. 16–33 (2013)
2013
Cited alongside, same era.
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: International conference on machine learning. pp. 1310–1318 (2013)
2013
Cited alongside, same era.
Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: International Conference on Machine Learning. pp. 1737–1746 (2015)
2015
Cited alongside, same era.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. pp. 308–318. ACM (2016)
2016
Cited alongside, same era.
Aono, Y., Hayashi, T., Phong, L.T., Wang, L.: Privacy-preserving logistic regression with distributed data sources via homomorphic encryption. IEICE Transactions on Information and Systems 99
2016
Cited alongside, same era.
Aono, Y., Hayashi, T., Wang, L., Moriai, S., et al.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Transactions on Information Forensics and Security 13
2018
Later among the works it cites.
Duchi, J.C., Jordan, M.I., Wainwright, M.J.: Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association 113
2018
Later among the works it cites.
Stich, S.U., Cordonnier, J.B., Jaggi, M.: Sparsified SGD with memory. In: Advances in Neural Information Processing Systems. pp. 4447–4458 (2018)
2018
Later among the works it cites.
2019
Closest in time.
2019
Closest in time.
Balle, B., Bell, J., Gascón, A., Nissim, K.: Private summation in the multi-message shuffle model. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security (2020)
2020
Closest in time.
Hartmann, V., Modi, K., Pujol, J.M., West, R.: Privacy-preserving classification with secret vector machines. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management (2020)
2020
Closest in time.
Hern, A.: Fitness tracking app gives away location of secret US army bases. https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases (2018), retrieved May 22, 2023
2023
Closest in time.
Rosenberg, M., Confessore, N., Cadwalladr, C.: How Trump consultants exploited the Facebook data of millions. https://www.nytimes.com/2018/03/17/us/politics/cambridge-analytica-trump-campaign.html (2018), retrieved May 22, 2023
2023
Closest in time.