# Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation

arXiv: https://arxiv.org/abs/2409.03796 · HTML: https://mlsanity.com/paper/2409.03796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Concentrated Differential Privacy](https://mlsanity.com/paper/1603.01887/) (2016)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Overlearning Reveals Sensitive Attributes](https://mlsanity.com/paper/1905.11742/) (2019)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [An Information Theoretic Interpretation to Deep Neural Networks](https://mlsanity.com/paper/1905.06600/) (2019)
- [TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations](https://mlsanity.com/paper/2005.11480/) (2020)
- [Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots](https://mlsanity.com/paper/2307.09579/) (2023)
- [VSMask: Defending Against Voice Synthesis Attack via Real-Time Predictive Perturbation](https://mlsanity.com/paper/2305.05736/) (2023)
- [PrivTrace: Differentially Private Trajectory Synthesis by Adaptive Markov Model](https://mlsanity.com/paper/2210.00581/) (2022)
- [DYSAN: Dynamically sanitizing motion sensor data against sensitive inferences through adversarial networks](https://mlsanity.com/paper/2003.10325/) (2020)
- [Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis](https://mlsanity.com/paper/1710.06564/) (2017)
- [Adversarial Information Factorization](https://mlsanity.com/paper/1711.05175/) (2017)
- [PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection](https://mlsanity.com/paper/2309.06960/) (2023)

## Abstract (arXiv)

Smartphones and wearable devices have been integrated into our daily lives, offering personalized services. However, many apps become overprivileged as their collected sensing data contains unnecessary sensitive information. For example, mobile sensing data could reveal private attributes (e.g., gender and age) and unintended sensitive features (e.g., hand gestures when entering passwords). To prevent sensitive information leakage, existing methods must obtain private labels and users need to specify privacy policies. However, they only achieve limited control over information disclosure. In this work, we present Hippo to dissociate hierarchical information including private metadata and multi-grained activity information from the sensing data. Hippo achieves fine-grained control over the disclosure of sensitive information without requiring private labels. Specifically, we design a latent guidance-based diffusion model, which generates multi-grained versions of raw sensor data conditioned on hierarchical latent activity features. Hippo enables users to control the disclosure of sensitive information in sensing data, ensuring their privacy while preserving the necessary features to meet the utility requirements of applications. Hippo is the first unified model that achieves two goals: perturbing the sensitive attributes and controlling the disclosure of sensitive information in mobile sensing data. Extensive experiments show that Hippo can anonymize personal attributes and transform activity information at various resolutions across different types of sensing data.