# Protecting Sensory Data against Sensitive Inferences

arXiv: https://arxiv.org/abs/1802.07802 · HTML: https://mlsanity.com/paper/1802.07802/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2018.

## Built on by (most cited first, 30 of 45)

- [Assessing the State of Self-Supervised Human Activity Recognition using Wearables](https://mlsanity.com/paper/2202.12938/) (2022)
- [Socially Responsible AI Algorithms: Issues, Purposes, and Challenges](https://mlsanity.com/paper/2101.02032/) (2021)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data](https://mlsanity.com/paper/2102.06073/) (2021)
- [RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data](https://mlsanity.com/paper/2411.18822/) (2024)
- [iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets](https://mlsanity.com/paper/2406.01646/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition](https://mlsanity.com/paper/2406.11914/) (2024)
- [Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition](https://mlsanity.com/paper/2209.11750/) (2022)
- [Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition -- And Ways to Overcome Them](https://mlsanity.com/paper/2408.12023/) (2024)
- [Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity](https://mlsanity.com/paper/2306.13735/) (2023)
- [Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition](https://mlsanity.com/paper/2310.14390/) (2023)
- [Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review](https://mlsanity.com/paper/2403.15422/) (2024)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [Dynamic User-controllable Privacy-preserving Few-shot Sensing Framework](https://mlsanity.com/paper/2508.03989/) (2025)
- [Dynamic Black-box Backdoor Attacks on IoT Sensory Data](https://mlsanity.com/paper/2511.14074/) (2025)
- [Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2109.02054/) (2021)
- [Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition](https://mlsanity.com/paper/2306.01108/) (2023)
- [Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2211.06173/) (2022)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2203.12230/) (2022)
- [Efficient Deep Clustering of Human Activities and How to Improve Evaluation](https://mlsanity.com/paper/2209.08335/) (2022)
- [Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning](https://mlsanity.com/paper/2209.00990/) (2022)
- [Assessing Differentially Private Variational Autoencoders under Membership Inference](https://mlsanity.com/paper/2204.07877/) (2022)
- [Human Activity Recognition Using Self-Supervised Representations of Wearable Data](https://mlsanity.com/paper/2304.14912/) (2023)
- [Don't freeze: Finetune encoders for better Self-Supervised HAR](https://mlsanity.com/paper/2307.01168/) (2023)
- [Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition](https://mlsanity.com/paper/2310.11950/) (2023)
- [MuJo: Multimodal Joint Feature Space Learning for Human Activity Recognition](https://mlsanity.com/paper/2406.03857/) (2024)
- [FedAli: Personalized Federated Learning Alignment with Prototype Layers for Generalized Mobile Services](https://mlsanity.com/paper/2411.10595/) (2024)
- [Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning](https://mlsanity.com/paper/2505.06454/) (2025)

## Abstract (arXiv)

There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacy-preserving sensor data transmission by appropriately transforming raw sensor data. The objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two real-world datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50%, the target random guess, from more than 90% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors.