Federated Learning (FL) enables model training on distributed devices while preserving data privacy. However, malicious clients can submit fabricated model updates to fraudulently obtain training rewards, a behavior known as free-rider attacks. Existing detection-based solutions analyze anomalies in model updates but lack direct evidence of local training, making it fail to fully prevent free-riders. To address this limitation, we propose zkVFL, a verifiable FL framework leveraging Zero-Knowledge Proofs (ZKP) to ensure the integrity of local training while preserving privacy. To reduce the computational overhead of proof generation in ZKP, zkVFL introduces two novel techniques: (i) anomaly-aware client sampling to selectively perform ZKP verification and (ii) A recursive ZKP protocol (ReMPoT), incorporating a pruning-based layer selection technique, reduces proof generation costs. Experimental results demonstrate that zkVFL improves the accuracy and convergence of FL training under free-rider attacks while significantly reducing the computational and memory overhead of proof generation on resource-constrained devices.

A privacy-preserving collaborative query protocol for distributed multi-server systems (DMS-P$^{2}$CQ) enables a querying party to interact with multiple independent servers using an identifier$x_{u}$and receive a categorical decision (e.g.,Good/Moderate/Poor) determined by the total number of servers whose datasets contain$x_{u}$. This setting is motivated by financial applications such as credit assessment, where the querying party must not reveal$x_{u}$to data-owning institutions, and each institution must protect its proprietary user list. To meet both the functionality and privacy requirements of the querying party and the servers, we present two protocols that represent a privacy-efficiency trade-off. DMS-P$^{2}$CQ$_{1}$is a lightweight protocol inspired by OPRF-based PSI. It achieves identifier privacy and hides the identifier-to-server membership relation from the querying party via a two-stage OPRF with an aggregation/re-randomization server. However, it reveals the aggregate count to a randomly selected leader server and relies on a non-collusion assumption between two special servers. DMS-P$^{2}$CQ$_{2}$strengthens privacy by secret-sharing the count so that no single party learns the true aggregate count and removes the need for a trusted re-randomization server. We prove the security of two protocols under the semi-honest model. Experimental results on our local testbed show that with 20 servers and a dataset size of$2^{20}$, the runtimes for DMS-P$^{2}$CQ$_{1}$and DMS-P$^{2}$CQ$_{2}$are approximately 8.034s and 16.697s, respectively.

We propose TrustMonitor, a lightweight runtime container protection framework designed for secure data sharing in cross-domain scenarios. TrustMonitor establishes a trusted container execution environment through the coordinated use of memory isolation, authenticated memory access, and identity binding. It leverages Extended Page Tables (EPT) to enforce hardware-level separation between containers and the host OS, forming the foundation for isolating sensitive data from untrusted system components. Building upon this isolation, TrustMonitor integrates software-emulated Pointer Authentication Codes (PAC) to authorize memory access on a per-context basis, ensuring that only verified execution paths can interact with protected container memory. To complete the trust chain, each container is bound to a virtual Trusted Platform Module (vTPM), which performs integrity measurements during instantiation and safeguards runtime secrets. Collectively, they establish a trusted execution foundation that mitigates kernel-level threats and runtime compromise, thereby securing sensitive data throughout its lifecycle in cross-domain sharing environments. Experimental results demonstrate that TrustMonitor effectively safeguards the confidentiality and integrity of shared data. Furthermore, it achieves these security guarantees with less than 15% computational overhead and no more than 25% latency increase, making it a practical and efficient solution for secure cross-domain data sharing environments.