Privacy in Cloud Computing through Immersion-based Coding: Conclusion and References Ali Guerra | usagoldmines.com

Desk of Hyperlinks

Abstract and Introduction

Problem Formulation

Affine Solution to Problem 1

Privacy Guarantee

General Guidelines for Implementation

Case Studies

Conclusion and References

VII. CONCLUSION

On this paper, we’ve developed a privacy-preserving framework for the implementation of distant dynamical algorithms within the cloud. It’s constructed on the synergy of random coding and system immersion instruments from management concept to guard non-public data. We’ve got devised a synthesis process to design the dynamics of a coding scheme for privateness and a higher-dimensional system referred to as goal algorithm such that trajectories of the usual dynamical algorithm are immersed/embedded in its trajectories, and it operates on randomly encoded higher-dimensional knowledge. Random coding was formulated on the consumer facet as a random change of coordinates that maps authentic non-public knowledge to a higher-dimensional house. Such coding enforces that the goal algorithm produces an encoded higher-dimensional model of the utility of the unique algorithm that may be decoded on the consumer facet.

The proposed immersion-based coding scheme offers the identical utility as the unique algorithm (i.e., when no coding is employed to guard in opposition to knowledge inference), (virtually) reveals no details about non-public knowledge, will be utilized to large-scale algorithms, is computationally environment friendly, and gives any desired degree of differential privateness with out degrading the algorithm utility.

REFERENCES

[1] S. R. Rajagopalan, L. Sankar, S. Mohajer, and H. V. Poor, “Good meter privateness: A utility-privacy framework,” in Proceedings of the IEEE Worldwide Convention on Good Grid Communications (SmartGridComm), 2011, pp. 190–195.

[2] O. Tan, D. Gund ¨ uz, and H. V. Poor, “Growing sensible meter privateness ¨ by way of power harvesting and storage gadgets,” IEEE Journal on Chosen Areas in Communications, vol. 31, pp. 1331–1341, 2013.

[3] Z. Huang, Y. Wang, S. Mitra, and G. E. Dullerud, “On the price of differential privateness in distributed management techniques,” in Proceedings of the third Worldwide Convention on Excessive Confidence Networked Techniques, 2014, pp. 105–114.

[4] and M. Gruteser, , and A. Alrabady, “Enhancing safety and privateness in traffic-monitoring techniques,” IEEE Pervasive Computing, vol. 5, pp. 38–46, 2006.

[5] R. H. Weber, “Web of issues – new safety and privateness challenges,” Laptop Legislation and Safety Evaluate, vol. 26, pp. 23–30, 2010.

[6] Z. Xiao and Y. Xiao, “Safety and privateness in cloud computing,” IEEE communications surveys & tutorials, vol. 15, no. 2, pp. 843–859, 2012.

[7] J. L. Ny and G. J. Pappas, “Differentially non-public filtering,” IEEE Transactions on Automated Management, vol. 59, pp. 341–354, 2014.

[8] R. M. Ferrari and A. M. Teixeira, Security, Safety and Privateness for CyberPhysical Techniques. Springer, 2021.

[9] T. Tanaka, M. Skoglund, H. Sandberg, and Ok. H. Johansson, “Directed data as privateness measure in cloud-based management,” arXiv preprint arXiv:1705.02802, 2017.

[10] C. Murguia, I. Shames, F. Farokhi, and D. Nesiˇ c, “On privateness of ´ quantized sensor measurements by way of additive noise,” in Proceedings of the 57th IEEE Convention on Resolution and Management (CDC), 2018.

[11] N. Wang, W. Zhou, J. Wang, Y. Guo, J. Fu, and J. Liu, “Safe and environment friendly similarity retrieval in cloud computing based mostly on homomorphic encryption,” IEEE Transactions on Data Forensics and Safety, 2024.

[12] M. S. Prakash and N. Monshizadeh, “Privateness-preserving cloud computation of algebraic riccati equations,” IEEE Management Techniques Letters, 2024.

[13] F. Farokhi, Privateness in Dynamical Techniques. Springer, 2020. [14] F. Farokhi and H. Sandberg, “Optimum privacy-preserving coverage utilizing constrained additive noise to attenuate the fisher data,” in Proceedings of the IEEE 56th Annual Convention on Resolution and Management (CDC), 2017.

[15] C. Murguia, I. Shames, F. Farokhi, D. Nesiˇ c, and H. V. Poor, “On privateness ´ of dynamical techniques: An optimum probabilistic mapping method,” IEEE Transactions on Data Forensics and Safety, 2021.

[16] H. Hayati, C. Murguia, and N. Van De Wouw, “Finite horizon privateness of stochastic dynamical techniques: A synthesis framework for gaussian mechanisms,” in 2021 sixtieth IEEE Convention on Resolution and Management (CDC). IEEE, 2021, pp. 5607–5613.

[17] H. Hayati, N. van de Wouw, and C. Murguia, “Infinite horizon privateness in networked management techniques: Utility/privateness tradeoffs and design instruments,” in 2023 62nd IEEE Convention on Resolution and Management (CDC), 2023, pp. 1847–1852.

[18] C. Dwork, “Differential privateness: A survey of outcomes,” in Idea and Purposes of Fashions of Computation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 1–19.

[19] J. Wan, A. Lopez, and M. A. A. Faruque, “Bodily layer key era: Securing wi-fi communication in automotive cyber-physical techniques,” ACM Transactions on Cyber-Bodily Techniques, vol. 3, no. 2, pp. 1–26, 2018.

[20] Y. Shoukry, Ok. Gatsis, A. Alanwar, G. J. Pappas, S. A. Seshia, M. Srivastava, and P. Tabuada, “Privateness-aware quadratic optimization utilizing partially homomorphic encryption,” in Proceedings of the IEEE fifty fifth Convention on Resolution and Management (CDC), 2016, pp. 5053–5058.

[21] B. Joshi, B. Joshi, A. Mishra, V. Arya, A. Ok. Gupta, and D. Perakovic,´ “A comparative research of privacy-preserving homomorphic encryption strategies in cloud computing,” Worldwide Journal of Cloud Purposes and Computing (IJCAC), vol. 12, no. 1, pp. 1–11, 2022.

[22] P. Paillier, “Public-key cryptosystems based mostly on composite diploma residuosity lessons,” in Worldwide convention on the speculation and functions of cryptographic strategies. Springer, 1999, pp. 223–238.

[23] C. Murguia, F. Farokhi, and I. Shames, “Safe and personal implementation of dynamic controllers utilizing semi homomorphic encryption,” IEEE Transactions on Automated Management, vol. 65, no. 9, pp. 3950–3957, 2020.

[24] J. Kim, D. Kim, Y. Track, H. Shim, H. Sandberg, and Ok. H. Johansson, “Comparability of encrypted management approaches and tutorial on dynamic techniques utilizing lwe-based homomorphic encryption,” arXiv preprint arXiv:2210.05560, 2022.

[25] A. Astolfi and R. Ortega, “Immersion and invariance: A brand new device for stabilization and adaptive management of nonlinear techniques,” IEEE Transactions on Automated management, vol. 48, no. 4, pp. 590–606, 2003.

[26] H. Hayati, C. Murguia, and N. van de Wouw, “Privateness-preserving federated studying through system immersion and random matrix encryption,” in 2022 IEEE 61st Convention on Resolution and Management (CDC). IEEE, 2022, pp. 6776–6781.

[27] H. Hayati, S. Heijmans, L. Persoon, C. Murguia, and N. van de Wouw, “Mo-0304 privacy-preserving federated studying for radiotherapy functions,” Radiotherapy and Oncology, vol. 182, pp. S238–S240, 2023.

[28] H. Hayati, N. van de Wouw, and C. Murguia, “Immersion and invariance-based coding for privateness in distant anomaly detection,” IFACPapersOnLine, vol. 56, no. 2, pp. 11 191–11 196, 2023.

[29] A. Astolfi, D. Karagiannis, and R. Ortega, Nonlinear and adaptive management with functions. Springer, 2008, vol. 187. [30] A. Isidori and C. Byrnes, “Output regulation of nonlinear techniques,” IEEE Transactions on Automated management, vol. 35, no. 2, pp. 131–140, 1990.

[31] F. Delli Priscoli, C. Byrnes, and A. Isidori, “Output regulation of unsure nonlinear techniques,” 1997.

[32] M. W. Hirsch, S. Smale, and R. L. Devaney, Differential equations, dynamical techniques, and an introduction to chaos. Educational press, 2012.

[33] N. Schluter, P. Binfet, and M. S. Darup, “Cryptanalysis of ran- ¨ dom affine transformations for encrypted management,” arXiv preprint arXiv:2304.06582, 2023.

[34] J. Upadhyay, “Random projections, graph sparsification, and differential privateness,” in Worldwide Convention on the Idea and Software of Cryptology and Data Safety. Springer, 2013, pp. 276–295.

[35] C. Dwork and A. Roth, “The algorithmic foundations of differential privateness,” Foundations and Developments in Theoretical Laptop Science, vol. 9, pp. 211–407, 2014.

[36] W. Diffie and M. E. Hellman, “New instructions in cryptography,” in Safe communications and uneven cryptosystems. Routledge, 2019, pp. 143–180.

[37] C. E. Shannon, “Communication concept of secrecy techniques,” The Bell system technical journal, vol. 28, no. 4, pp. 656–715, 1949.

[38] C. Wang and S. Ju, “E book cipher with infinite key house,” in 2008 Worldwide Symposium on Data Science and Engineering, vol. 1. IEEE, 2008, pp. 456–459.

[39] C. Dwork, F. McSherry, Ok. Nissim, and A. Smith, “Differential privateness—a primer for the perplexed,”,” Joint UNECE/Eurostat work session on statistical knowledge confidentiality, vol. 11, 2011.

[40] B. Knott, S. Venkataraman, A. Hannun, S. Sengupta, M. Ibrahim, and L. van der Maaten, “Crypten: Safe multi-party computation meets machine studying,” Advances in Neural Data Processing Techniques, vol. 34, pp. 4961–4973, 2021.

[41] Y. Aono, T. Hayashi, L. Wang, S. Moriai et al., “Privateness-preserving deep studying through additively homomorphic encryption,” IEEE Transactions on Data Forensics and Safety, vol. 13, no. 5, pp. 1333–1345, 2017.

[42] Ok. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor, “Federated studying with differential privateness: Algorithms and efficiency evaluation,” IEEE Transactions on Data Forensics and Safety, vol. 15, pp. 3454–3469, 2020.

[43] S. Ruder, “An summary of gradient descent optimization algorithms,” arXiv preprint arXiv:1609.04747, 2016.

[44] J. Tang, C. Deng, and G.-B. Huang, “Excessive studying machine for multilayer perceptron,” IEEE transactions on neural networks and studying techniques, vol. 27, no. 4, pp. 809–821, 2015.

[45] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based studying utilized to doc recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, 1998.

[46] X. Jia, X. Chen, S. Xu, B. Zhang, and Z. Zhang, “Adaptive output suggestions management of nonlinear time-delay techniques with utility to chemical reactor techniques,” IEEE Transactions on Industrial Electronics, vol. 64, no. 6, pp. 4792–4799, 2017.