Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity recognition and step counting, this openness has also enabled unregulated inference of sensitive user traits, such as gender, age, and even identity, without user consent. Existing privacy-preserving techniques, such as GAN-based obfuscation or differential privacy, typically require access to the full input sequence, introducing latency that is incompatible with real-time scenarios. Worse, they tend to distort temporal and semantic patterns, degrading the utility of the data for benign tasks like activity recognition. To address these limitations, we propose the Predictive Adversarial Transformation Network (PATN), a real-time privacy-preserving framework that leverages historical signals to generate adversarial perturbations proactively. The perturbations are applied immediately upon data acquisition, enabling continuous protection without disrupting application functionality. Experiments on two datasets demonstrate that PATN substantially degrades the performance of privacy inference models, achieving Attack Success Rate (ASR) of 40.11% and 44.65% (reducing inference accuracy to near-random) and increasing the Equal Error Rate (EER) from 8.30% and 7.56% to 41.65% and 46.22%. On ASR, PATN outperforms baseline methods by 16.16% and 31.96%, respectively.

Smartphone addiction among child users is becoming a severe global social problem. Uncontrolled and unsupervised use of smartphones by children has posed a significant threat to the health and property of both children and their parents. Automatic identification of child users on smartphones can be an effective way to alleviate this problem. Unfortunately, existing works usually require additional input devices like cameras for face biometrics or additional applications for users’ specific touch-interaction behavior to identify child users, leading to poor user experience and privacy concerns. This paper develops a novel, implicit and continuous system, named ChildShield, for child identification on smartphones. Specifically, our system providing a built-in data acquisition service can automatically and real-timely collect users’ behavioral data in a non-conscious and privacy-preserving manner. We build a large-scale database by collecting users’ operations in 5 complex and popular mobile game applications on 12 different models of smartphones from 1875 subjects. Based on the feature extracted from multi-finger interaction data in realistic and complex usage scenarios, ChildShield can learn the discriminative behavioral patterns for accurate child identification using the specifically designed deep learning-based classifiers. Then when a child user is identified, the pre-setting subsequent operation like Enable Kids Mode in ChildShield can be executed to provide a protective shield for children. The effectiveness of ChildShield is validated on the created database. Our approach significantly outperforms existing methods, achieves an EER of 4.38% for child identification, and performs an even lower EER of 2.12% for the younger age group.

Owing to the boom of smartphone industries, the expansion of phone users has also been significant. Besides adults, children and elders have also begun to join the population of daily smartphone users. Such an expansion indeed facilitates the further exploration of the versatility and flexibility of digitization. However, these new users may also be susceptible to issues such as addiction, fraud, and insufficient accessibility. To fully utilize the capability of mobile devices without breaching personal privacy, we build the first corpus for age group recognition on smartphones with more than 1,445,087 unrestricted actions from 2,100 subjects. Then a series of heuristically-selected and neurally-guided features are proposed to increase the separability of the above dataset. Finally, we develop AgeCare, the first implicit and continuous system incorporated with bottom-to-top functionality without any restriction on user-phone interaction scenarios, for accurate age group recognition and age-tailored assistance on smartphones. Our system performs impressively well on this dataset and significantly surpasses the state-of-the-art methods.