With the integration of intelligent medical devices and wireless sensor networks, telemedicine queries have significantly enhanced the efficiency and accessibility of medical services. However, the security and privacy of medical query records remain a critical challenge. While existing privacy-preserving query frameworks in Wireless Body Area Networks (WBANs) primarily focus on protecting the privacy of medical records, they often lack robust mechanisms for authenticating query users. This deficiency may enable malicious entities to gain unauthorized access, thereby resulting in data leakage. To address this issue, we propose a privacy-preserving query scheme with unlinkability by integrating double-Schnorr-based anonymous authentication and 1-out-of-n oblivious transfer (OT). Specifically, anonymous authentication is achieved via a double-Schnorr-based credential construction with session-dependent pseudonym blinding, enabling the server to verify user legitimacy without revealing users’ real identities. OT is implemented through a short-hash-based keyword partition mechanism and masked encryption, ensuring that the curious server cannot infer user queries while fulfilling them correctly. Additionally, query privileges are restricted to legitimate users by verifying anonymous credentials issued during the registration phase. The formal security analysis confirms that the proposed scheme achieves essential security attributes, including anonymous authentication, query privacy, and unlinkability. Experimental results demonstrate that the proposed scheme not only integrates anonymous authentication but also outperforms baseline schemes in both computational and communication efficiency. Therefore, the proposed scheme is more suitable for real-world deployment within IoT-based healthcare ecosystems.

Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more broadly, due to the high costs and human efforts involved. There has been significant interest in imitation learning for bimanual dexterous robots, like humanoids. Unfortunately, data collection is even more challenging here due to the difficulty of simultaneously controlling the two arms and multi-fingered hands. Automated data generation in simulation is a compelling, scalable alternative to fuel this need for training data. To this end, we introduce DexMimicGen, a large-scale automated data generation system that synthesizes trajectories from a handful of human demonstrations for bimanual robots with dexterous hands. We present a collection of simulation environments in the setting of bimanual dexterous manipulation, spanning a range of manipulation behaviors and different requirements for coordination among the two arms. We generate 21K demos across these tasks from just 60 source human demos and study the effect of several data generation and policy learning decisions on agent performance. Finally, we present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task. Generated datasets, simulation environments and additional results are at dexmimicgen.github.io.