Proof-of-Stake (PoS) blockchain protocols have gained increasing prominence in recent years due to their energy efficiency. In PoS systems, users stake tokens to become validators. Those who stake more tokens are elected to propose new blocks with higher probabilities and possess greater voting power in consensus decisions. At first glance, threshold signatures appear to be a promising mechanism for aggregating multiple block attestations in PoS blockchain protocols. However, traditional threshold signatures treat each participant equally in signature generation, disregarding the differing weights of participants. This lack of weight consideration poses a challenge in PoS blockchains, where validators inherently have varying levels of influence. To address this issue, a weighted threshold signature scheme is necessary—one that accounts for the distinct weights of signers, reflecting their respective voting power in PoS blockchains. Despite this need, no existing weighted threshold signature schemes are resistant to attacks by quantum computing. To fill this gap in the literature, we propose the first lattice-based weighted threshold signature scheme, proven secure under the module short integer solution (MSIS) assumption in the random oracle model. Furthermore, we demonstrate the integration of this novel scheme into PoS blockchain protocols for block attestation. Finally, we implement our weighted threshold signature scheme and present its efficiency evaluation.

Energy-efficient proof-of-stake (PoS) consensus protocols in blockchain have gained much attention from academia and industry recently. Despite their potential advantages, PoS protocols have not been extensively deployed in the existing digital currency market due to inherent security concerns, e.g., long-range attacks. Such attacks enable an adversary to rewrite the entire transaction history of a blockchain, severely compromising its immutability. The puncturable signature provides an efficient solution against long-range attacks due to secret key leakage. More specifically, a signer can update the secret key with chosen messages selectively, while the public key is unchanged. Unfortunately, the existing puncturable signature schemes suffer from either updating the public key repeatedly or large key size, which makes them unsuitable for PoS protocols. To resolve these drawbacks, we adopt a different approach to performing key puncture operations and propose a generic puncturable signature construction from delegated (key-policy) constrained signatures. We present a concrete puncturable signature scheme over lattices that is proven secure based on the short integer solution (SIS) assumption in the standard model.

In remote areas or emergency rescue scenarios, the lack of infrastructural network facilities, and the limited energy and computational resources of Internet of Medical Things (IoMT), IoMT devices pose a challenge for medical devices to handle a large number of computationally intensive tasks. To solve this problem, a computation offloading strategy that integrates Space-Air-Ground Integrated Network (SAGIN) and Mobile Edge Computing (MEC) technologies is proposed to achieve low-latency and energy-efficient edge healthcare. Unmanned aerial vehicle (UAV) and Low Earth Orbit (LEO) satellites are used to enhance network coverage, and the problem of maximizing system energy efficiency is constructed through joint UAV location deployment and offloading decisions. To optimize the system energy efficiency, an energy-efficient UAV 2D position deployment algorithm (PD-UAV 2D) based on medical IoMT device grouping is proposed. The algorithm determines the association relationship between UAVs and IoMT devices through the IoMT device grouping mapping strategy and dynamically adjusts the coverage radius of UAVs based on the idea of a minimum coverage circle to achieve energy- efficient UAV deployment. In addition, considering the urgency of the task and the residual energy state of the medical devices, a computational offloading mechanism with task priority is designed to provide offloading services for the medical devices using UAVs and LEOs. Finally, an adaptive particle swarm (APSO) algorithm is used to solve the problem. Simulations show that the proposed scheme can significantly improve the system's eneray efficiency.

Puncturable encryption (PE), introduced by Green and Miers at IEEE S$&$P 2015, allows recipients to update their decryption keys to revoke decryption capability for selected messages without communicating with senders. In general, it allows users to control which ciphertexts their keys may decrypt. The notion of PE has been found very useful in many applications, such as asynchronous messaging systems, group messaging systems, public-key watermarking schemes, secure cloud emails, and many more. In this paper, we introduce a new primitive called hierarchical identity-based puncturable encryption (HIBPE) that enhances the concept of PE by allowing more general key delegation and flexible key puncture. It enhances the capability of the data owner for multi-level encrypted data sharing within a group of users by delegating the decryption keys of the users in higher-levels to generate decryption keys for the users in lower-levels. Moreover, it allows users to puncture (update) their decryption keys on tags so that a decryption key punctured on a tag can no longer decrypt ciphertexts under this tag. In addition, to control access to the users' data, the higher-level users can further puncture the delegated keys (for lower-level users) with some tags such that the part of the owner's data is labeled by the punctured tags will no longer be accessible by the lower-level users. These features offer an efficient and flexible solution for encrypted data sharing as well as data-access control mechanisms in a hierarchical setting. We propose the formal definition and security model for HIBPE schemes and provide a concrete HIBPE scheme based on the hardness of the learning with errors problem in the standard model. Further, we provide a generic construction of forward secure hierarchical identity-based encryption (fs-HIBE) from HIBPE, which enables the first quantum-safe construction of fs-HIBE in the standard model. Moreover, this is the first fs-HIBE construction by exploring the concept of PE. The proposed fs-HIBE provides quantum-safe protection for secret keys from exposure in multi-level encrypted data sharing by evolving the keys with time.

A two-stage pipeline architecture for pedestrian detection processor, which embeds the support vector machine (SVM) classifier into the Histogram of Oriented Gradients (HOG) normalization module is proposed. This architecture can effectively reduce hardware resource consumption and can perform pedestrian detection task real-timely. Also, an algorithm is proposed to avoid duplicated detection automatically. The architecture is verified on Spartan-6 FPGA for SVGA resolution video (800×600) at 47 fps/100MHz.

The growing population of seniors leads to the need for an intelligent surveillance system to ensure the safety of the elders at home. Fall is one kind of the most seriously life-threatening emergencies for elderly people. Fall detection system based on video surveillance provides an efficient solution for detecting fall events automatically by analyzing human behaviors. In this paper, we propose a context-based fall detection system by analyzing human motion and posture using hidden Markov model (HMM) and relevance vector machine (RVM) respectively. Additionally, we integrate homography to deal with falls in any direction. The system is validated on an open fall database and our own video dataset. Experimental results demonstrate that our method achieves high robustness and accuracy in detecting different kinds of falls and runs at a real-time speed.

We present a detection based tracking algorithm for tracking melanosomes (organelles containing melanin) in time lapse image sequences imaged using bright field microscopy. Due to heavy imaging noise detecting all the melanosomes accurately in every frame is difficult. Therefore, two sets of imperfect detections are used in a unified probabilistic approach to simultaneously perform melanosome detection and tracking. We propose a novel iterative algorithm which jointly estimates the optimal set of detections and track results in every iteration from the previous tracks and detections. Our algorithm obtains significantly better tracking results than the state of the art tracking-by-detection algorithm.