Outsourcing storage has emerged as an effective solution to manage the increasing volume of data. With the popularity of pay-as-you-go payment models in outsourcing storage, data auditing schemes that prioritize timeliness can be valuable evidence for elastic bill settlement. Unfortunately, existing data auditing schemes do not sufficiently consider timeliness during auditing. Furthermore, practical data auditing schemes should have the capability to check the integrity of scalable data. In this paper, we propose a blockchain-based dynamic data auditing scheme with strong timeliness to ensure that data stored in outsourcing storage systems remain intact. Our scheme encapsulates timestamps into homomorphic verifiable tags to simultaneously check data integrity and timestamp validity. To achieve dynamicity, we utilize the Merkle hash tree to store the tags, allowing for block-level dynamic operations. Additionally, by leveraging the transparency, non-repudiation, and tamper resistance of blockchain technology, we design a blockchain-based data auditing framework to prevent malicious behavior from all entities. We then formally prove the soundness and privacy of our scheme. Finally, we conduct theoretical analysis and experimental evaluation to demonstrate that the performance of our scheme is of acceptable efficiency to existing works in terms of computation cost, communication overhead, and storage overhead.

Federated Learning (FL) is vulnerable to backdoor attacks in which attackers inject malicious behaviors into the global model. To counter these attacks, existing works mainly introduce sophisticated defenses by analyzing model parameters and utilizing robust aggregation strategies. However, we find that FL systems can still be attacked by exploiting their inherent complexity. In this paper, we propose a novel three-stage backdoor attack strategy named FLARE: A Backdoor Attack to Federated Learning with Refined Evasion, which is designed to operate under the radar of conventional defense strategies. Our proposal begins with a trigger inspection stage to leverage the initial susceptibilities of FL systems, followed by a trigger insertion stage where the synthesized trigger is stealthily embedded at a low poisoning rate. Finally, the trigger is amplified to increase the attack’s success rate during the backdoor activation stage. Experiments on the effectiveness of FLARE show significant enhancements in both the stealthiness and success rate of backdoor attacks across multiple federated learning environments. In particular, the success rate of our backdoor attack can be improved by up to 45× compared to existing methods.

Agentic AI, particularly in the form of embodied agents such as unmanned aerial vehicle (UAV) swarms, requires secure and efficient communication to enable collaborative decision-making. However, the open wireless links these agents use for coordination are vulnerable to eavesdropping and adversarial attacks. Because each command capsule must reach many recipients simultaneously, classical per-recipient public-key encryption inflates airtime, drains batteries, and complicates key management. Moreover, a captured drone can leak its secret key, so the compromised unit must be identifiable without grounding the entire fleet, and the cryptosystem must remain secure against future quantum adversaries. In this paper, we propose a traceable hybrid multi-recipient key encapsulation mechanism (mKEM) to meet these requirements, called SKYTRA-KEM. Our scheme enables secure group-oriented communication for multi-agent systems, where a single compact ciphertext serves all swarm members that share a group public key while holding distinct, traceable secret keys. The experiment shows that, for a swarm of 100 drones arranged in five groups, the scheme reduces sender CPU time from 1.31 ms to 0.20 ms and cuts broadcast size from 78.1 kB to 4.0 kB. A qualitative comparison with recent mKEM constructions further demonstrates the unique combination of hybrid security, traceability, and bandwidth efficiency provided by SKYTRA-KEM. These results demonstrate an accountable, quantum-resilient, and energy-efficient communication framework designed for the resource-constrained nature of embodied agents.