Privacy Aware Multimedia Computation

Multidimensional streaming signals like audio, videos, and multi-spectral imagery are truly the biggest of the Big Data. Collected by sensors at the network edge, these data often contain highly sensitive information and their misuse can lead to significant invasion of privacy. For example, networks of surveillance cameras have been used by some countries in tracking their citizens, and pervasive use of IoT devices in smart homes opens door to recording private behaviors and interactions. It is highly challenging to apply traditional privacy enhancing technologies to handle these data. Cryptographic techniques like homomorphic encryption and garbled circuits are too computationally intensive for any practical applications. The more efficient differential private schemes, on the other hand, rely on additive noise and may not be able to provide adequate protection on semantic contents.

In this project cluster, we have developed a number of techniques to process multidimensional signals while ensuring their privacy. First, I will talk about how to exploit the nature of the signals in speeding up cryptographic computation. Specifically, we have developed a novel optimized design of a garble-circuit based iris-code matching algorithm where we can achieve significant speedup using a common iris mask. Second, we have investigated the use of secret sharing as a faster and more convenient privacy platform for distributed signal computation. Secret sharing does not require key distribution and can achieve information theoretic security without long integer fields. However, secret sharing is prone to collusion attacks. We have proposed a peer-to-peer computational framework that combines cryptographic and game-theoretic countermeasures to prevent such attacks. Finally, we are currently studying generative adversarial network (GAN) and random neural networks to protect signal privacy in distributed deep learning. We use GAN to learn the statistics of sensitive facial data at local sites and generate privacy-preserving synthetic data for public centralized learning. While GAN at network edge can be computationally intensive, random neural network offers a much lighter weight privacy-enhancing protocol suitable for a broad range of IoT applications.

Selected Publications:

• Cheung, S.-C., M. Usman Rafique, and W.-T. Tan. 2018. Privacy-Preserving Distributed Deep Learning with Privacy Transformation. In IEEE International Workshop on Information Forensics and Security (WIFS 2018), 11-13 December, 2018, Hong Kong, China.
• Cheung, S.-C., H. Wildfeuer, M. Nikkah, X. Zhu, and W.-T. Tan. 2018. Learning Sensitive Images Using Generative Models. In IEEE International Conference on Image Processing (ICIP 2018), 7-10 October, 2016, Athens, Greece.
• Wang, Z., Y. Luo, and S.-C. Cheung. 2017. Information-Theoretic Secure Multi-Party Computation With Collusion Deterrence. IEEE Transactions on Information Forensics and Security, vol. 12, no. 4, April 2017, pp. 980-995.
• Wang, Z., and S.-C. Cheung. 2016. On Privacy Preference in Collusion-deterrence Games for Secure Multi-Party Computation. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), 20-25 March 2016, Shanghai, China.
• Wang, Z. and S.-C. Cheung. 2014. Protecting Privacy in Signal Processing. IEEE Potentials, vol. 33, issue 3, pp. 32-37.
• Wang, Z., Y. Luo and S.-C. Cheung. 2014. Efficient Multi-party Computation with Collusion-deterred Secret Sharing. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 4-9, 2014, pp. 7401-7405.
• Luo, Y., S.-C. Cheung, R. Lazzeretti, T. Pignata, and M. Barni. 2012. An Efficient Protocol for Private Iris-code Matching using Garbled Circuits. In IEEE International Conference on Image Processing (ICIP 2012), Sept. 30-Oct. 3, 2012, pp. 2653-2656.
• S. M. Esfahani, and S.-C. Cheung. 2012. Privacy Protected Image De-noising Using Secret Sharing. In IEEE International Conference on Image Processing (ICIP 2012), Sept. 30-Oct. 3, 2012, pp. 253-256.
• S. Yee, Y. Lou, J. Zhao and S.-C. Cheung. 2009. Anonymous Biometric Access Control. EURASIP Journal on Information Security, Volume 2009, Article 865259.
• Luo, Y., S.-C. Cheung and S. Ye. 2009. Anonymous Biometric Access Control Based on Homomorphic Encryption. Proceedings of the IEEE International Conference on Multimedia Expo (ICME 09), June 28- July 3, 2009, pp. 1046-1049.