Fetching the paper…
Reading the bibliography…
The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our data, ourselves: Privacy via distributed noise generation,” in Annual International Conference on the Theory and Applications of Cryptographic Techniques . Springer, 2006, pp. 486–503
2006
Earlier work this paper cites.
G. Griffin, A. Holub, and P. Perona, “Caltech-256 object category dataset,” 2007
2007
Earlier work this paper cites.
F. McSherry and I. Mironov, “Differentially private recommender systems: Building privacy into the netflix prize contenders,” in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining , 2009, pp. 627–636
2009
Earlier work this paper cites.
P. Kanerva, “Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors,” Cognitive computation , vol. 1, no. 2, pp. 139–159, 2009
2009
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar et al. , “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , 2016, pp. 308–318
2016
Earlier work this paper cites.
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in International Conference on Machine Learning , 2016, pp. 201–210
2016
Cited alongside, same era.
S. Teerapittayanon, B. McDanel, and H.-T. Kung, “Distributed deep neural networks over the cloud, the edge and end devices,” in 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) . IEEE, 2017, pp. 328–339
2017
Cited alongside, same era.
H. Li, K. Ota, and M. Dong, “Learning iot in edge: Deep learning for the internet of things with edge computing,” IEEE network , vol. 32, no. 1, pp. 96–101, 2018
2018
Cited alongside, same era.
J. Wang, J. Zhang, W. Bao, X. Zhu, B. Cao, and P. S. Yu, “Not just privacy: Improving performance of private deep learning in mobile cloud,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 2407–2416
2018
M. Imani, Y. Kim, S. Riazi, J. Messerly, P. Liu, F. Koushanfar et al. , “A framework for collaborative learning in secure high-dimensional space,” in 2019 IEEE 12th International Conference on Cloud Computing (CLOUD) . IEEE, 2019, pp. 435–446
2019
Later among the works it cites.
S. Salamat, M. Imani, B. Khaleghi, and T. Rosing, “F5-hd: Fast flexible fpga-based framework for refreshing hyperdimensional computing,” in Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays . ACM, 2019, pp. 53–62
2019
Later among the works it cites.
M. Imani, S. Salamat, B. Khaleghi, M. Samragh, F. Koushanfar, and T. Rosing, “Sparsehd: Algorithm-hardware co-optimization for efficient high-dimensional computing,” in 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) . IEEE, 2019, pp. 190–198
2019
Later among the works it cites.
S. A. Osia, A. S. Shamsabadi, S. Sajadmanesh, A. Taheri, K. Katevas, H. R. Rabiee et al. , “A hybrid deep learning architecture for privacy-preserving mobile analytics,” IEEE Internet of Things Journal , 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Schmuck, L. Benini, and A. Rahimi, “Hardware optimizations of dense binary hyperdimensional computing: Rematerialization of hypervectors, binarized bundling, and combinational associative memory,” ACM Journal on Emerging Technologies in Computing Systems (JETC) , vol. 15, no. 4, pp. 1–25, 2019
2019
Cited alongside, same era.
A. Mitrokhin, P. Sutor, C. Fermüller, and Y. Aloimonos, “Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception,” Science Robotics , vol. 4, no. 30, p. eaaw6736, 2019
2019
Cited alongside, same era.
P. Neubert, S. Schubert, and P. Protzel, “An introduction to hyperdimensional computing for robotics,” KI-Künstliche Intelligenz , pp. 1–12
Cited in the paper.
“Uci machine learning repository,” http://archive.ics.uci.edu/ml/datasets/ISOLET
Cited in the paper.
2020
Closest in time.
F. Mireshghallah, M. Taram, P. Ramrakhyani, A. Jalali, D. Tullsen, and H. Esmaeilzadeh, “Shredder: Learning noise distributions to protect inference privacy,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems , 2020, pp. 3–18
2020
Closest in time.