Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder
Author:
Luo Qingcai1ORCID, Li Hui2
Affiliation:
1. School of Cyber Engineering, Xidian University, Xi’an 710126, China 2. School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Abstract
Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference51 articles.
1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Burstein;Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019,2019 2. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv, Available online: http://arxiv.org/abs/2010.11929. 3. Li, A., Guo, J., Yang, H., Salim, F.D., and Chen, Y. (2021, January 18–21). DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones. Proceedings of the IoTDI’21: International Conference on Internet-of-Things Design and Implementation, Charlottesville, VA, USA. 4. Ribeiro, M., Grolinger, K., and Capretz, M.A. (2015, January 9–11). MLaaS: Machine Learning as a Service. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA. 5. Emergence of Invariance and Disentanglement in Deep Representations;Achille;J. Mach. Learn. Res.,2018
|
|