Xin Yang
ABSTRACT
Generative models have shown great promise in synthesizing high-quality time-series data that resemble the sensor data generated by mobile and IoT devices, but do not reveal the user's private attributes. These synthesized data can be treated as the obfuscated version of the sensor data and sent to downstream applications. However, existing obfuscation techniques that rely on generative models require the user to enumerate all inferences they deem intrusive. This black-listing approach would inevitably result in privacy loss if the definition of intrusive inferences changes after releasing the obfuscated data. In this work, we propose a white-listed approach to sensor data obfuscation based on a guided denoising diffusion model and a surrogate model for the desired inference. We evaluate this obfuscation model on a human activity recognition dataset and show that the proposed obfuscation model provides an acceptable privacy-utility trade-off, without assuming knowledge of the private attributes.
- 2021 [Online]. ObscureNet Implementation. https://github.com/sustainablecomputing/ObscureNet. Accessed in 2021.Google Scholar
- 2023 [Online]. Anonymization Autoencoder Implementation. https://github. com/mmalekzadeh/motion-sense. Accessed in 2023.Google Scholar
- 2023 [Online]. UNet Implementation. https://github.com/openai/guideddiffusion. Accessed in 2023.Google Scholar
- Charikleia Chatzaki et al. 2016. Human daily activity and fall recognition using a smartphone's acceleration sensor. In International Conference on Information and Communication Technologies for Ageing Well and e-Health. Springer, 100--118.Google Scholar
- Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34 (2021), 8780--8794.Google Scholar
- Omid Hajihassani, Omid Ardakanian, and Hamzeh Khazaei. 2022. Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach. ACM Transactions on Internet of Things 3, 1, Article 8 (2022), 26 pages.Google ScholarDigital Library
- Omid Hajihassnai, Omid Ardakanian, and Hamzeh Khazaei. 2021. ObscureNet: Learning Attribute-invariant Latent Representation for Anonymizing Sensor Data. In Proceedings of the International Conference on Internet-of-Things Design and Implementation. 40--52.Google ScholarDigital Library
- Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33 (2020), 6840--6851.Google Scholar
- Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022).Google Scholar
- Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. 2022. Diffusionclip: Textguided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2426--2435.Google ScholarCross Ref
- Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho. 2021. Variational diffusion models. Advances in Neural Information Processing Systems 34 (2021), 21696--21707.Google Scholar
- Ang Li et al. 2020. TIPRDC: task-independent privacy-respecting data crowdsourcing framework for deep learning with anonymized intermediate representations. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 824--832.Google Scholar
- Ang Li et al. 2021. DeepObfuscator: Obfuscating Intermediate Representations with Privacy-Preserving Adversarial Learning on Smartphones. In Proceedings of the International Conference on Internet-of-Things Design and Implementation. 28--39.Google Scholar
- Sicong Liu et al. 2019. Privacy adversarial network: representation learning for mobile data privacy. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 4 (2019), 1--18.Google Scholar
- Mohammad Malekzadeh et al. 2019. Mobile sensor data anonymization. In Proceedings of the International Conference on Internet-of-Things Design and Implementation. 49--58.Google Scholar
- Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2021. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021).Google Scholar
- Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn. 2022. Diffusion autoencoders: Toward a meaningful and decodable representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10619--10629.Google ScholarCross Ref
- Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748--8763.Google Scholar
- Nisarg Raval et al. 2019. Olympus: Sensor Privacy through Utility Aware Obfuscation. Proc. Priv. Enhancing Technol. 2019, 1 (2019), 5--25.Google ScholarCross Ref
- Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning. PMLR, 2256--2265.Google Scholar
- Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020).Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017).Google Scholar
- Benjamin Weggenmann, Valentin Rublack, Michael Andrejczuk, Justus Mattern, and Florian Kerschbaum. 2022. DP-VAE: Human-readable text anonymization for online reviews with differentially private variational autoencoders. In Proceedings of the ACM Web Conference 2022. 721--731.Google ScholarDigital Library
- Xin Yang and Omid Ardakanian. 2022. Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning. arXiv preprint arXiv:2209.12046 (2022).Google Scholar
Index Terms
- Privacy through Diffusion: A White-listing Approach to Sensor Data Anonymization
Recommendations
From t-closeness to differential privacy and vice versa in data anonymization
k-anonymity and ε-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from including one individual in the data set. Whereas basic k-anonymity only ...
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning
This article proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneous settings where the sensor data have different underlying distributions. ...
IMR based Anonymization for Privacy Preservation in Data Mining
KMO '16: Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting SocietyPrivacy Preserving Data Mining (PPDM) is a data mining research area that aims to protect individual's personal information from unsolicited or unauthorized disclosure. Privacy relates to personal information that a person would not wish others to know ...
Comments