Haipeng Peng

Xiao Liu, Mingyuan Li, Guangsheng Yu et al.

Unlearning is challenging in generic learning frameworks with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework that enables fully parallel unlearning among models exhibiting inheritance. We use a chronologically Directed Acyclic Graph (DAG) to capture various unlearning scenarios occurring in model inheritance networks. Central to our framework is the Fisher Inheritance Unlearning (FIUn) method, designed to enable efficient parallel unlearning within the DAG. FIUn utilizes the Fisher Information Matrix (FIM) to assess the significance of model parameters for unlearning tasks and adjusts them accordingly. To handle multiple unlearning requests simultaneously, we propose the Merging-FIM (MFIM) function, which consolidates FIMs from multiple upstream models into a unified matrix. This design supports all unlearning scenarios captured by the DAG, enabling one-shot removal of inherited knowledge while significantly reducing computational overhead. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0% accuracy for unlearned labels while maintaining 94.53% accuracy for retained labels. For multi-class tasks, the accuracy is 1.07% for unlearned labels and 84.77% for retained labels. Our framework accelerates unlearning by 99% compared to alternative methods. Code is in https://github.com/MJLee00/Parallel-Unlearning-in-Inherited-Model-Networks.

Xiao Liu, Mingyuan Li, Xu Wang et al.

Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all inherited models complicates the process.In this paper, we introduce Blockchained Federated Unlearning (BlockFUL), a novel framework with a dual-chain structure comprising a live chain and an archive chain for enabling unlearning capabilities within Blockchained FL. BlockFUL introduces two new unlearning paradigms, i.e., parallel and sequential paradigms, which can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. These methods enhance the unlearning process across multiple inherited models by enabling efficient consensus operations and reducing computational costs. Our extensive experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL on CIFAR-10 and Fashion-MNIST datasets using AlexNet, ResNet18, and MobileNetV2 models.

Dawei Zhao, Haipeng Peng, Lixiang Li et al.

Recently, Mun et al. analyzed Wu et al.'s authentication scheme and proposed a enhanced anonymous authentication scheme for roaming service in global mobility networks. However, through careful analysis, we find that Mun et al.'s scheme is vulnerable to impersonation attacks, off-line password guessing attacks and insider attacks, and cannot provide user friendliness, user's anonymity, proper mutual authentication and local verification. To remedy these weaknesses, in this paper we propose a novel anonymous authentication scheme for roaming service in global mobility networks. Security and performance analyses show the proposed scheme is more suitable for the low-power and resource-limited mobile devices, and is secure against various attacks and has many excellent features.

Dawei Zhao, Haipeng Peng, Shudong Li et al.

Recently, Li et al. analyzed Lee et al.'s multi-server authentication scheme and proposed a novel smart card and dynamic ID based remote user authentication scheme for multi-server environments. They claimed that their scheme can resist several kinds of attacks. However, through careful analysis, we find that Li et al.'s scheme is vulnerable to stolen smart card and offline dictionary attack, replay attack, impersonation attack and server spoofing attack. By analyzing other similar schemes, we find that the certain type of dynamic ID based multi-server authentication scheme in which only hash functions are used and no registration center participates in the authentication and session key agreement phase is hard to provide perfect efficient and secure authentication. To compensate for these shortcomings, we improve the recently proposed Liao et al.'s multi-server authentication scheme which is based on pairing and self-certified public keys, and propose a novel dynamic ID based remote user authentication scheme for multi-server environments. Liao et al.'s scheme is found vulnerable to offline dictionary attack and denial of service attack, and cannot provide user's anonymity and local password verification. However, our proposed scheme overcomes the shortcomings of Liao et al.'s scheme. Security and performance analyses show the proposed scheme is secure against various attacks and has many excellent features.