A decentralized identity system that can provide users with selfsovereign digital identities to facilitate complete control over their own data is paramount to Web 3.0.The account system on blockchain is an ideal archetype for realizing Web 3.0 decentralized identity.However, a disadvantage of such completely anonymous identity system is that users can create multiple accounts without authentication to obfuscate their activities on the blockchain.In particular, the current anonymous blockchain account system cannot accurately register the social relationships and interactions between real human users, given the amorphous mappings between users and blockchain identities.This work proposes zkBID, a zero-knowledge blockchain-account-based Web 3.0 decentralized identity scheme, to overcome endemic mistrust in blockchain account systems.zkBID links souls (blockchain accounts) to humans (users' personhood credentials) in a one-to-one manner to truly reflect the social relationships and interactions between humans on the blockchain.zkBID conceals the one-to-one relationships between blockchain accounts and users' personhood credentials for privacy protection using zero-knowledge proofs and linkable ring signatures.Thus, with zkBID, the users' blockchain accounts are credibly anonymous.Importantly, zkBID is fully decentralized: all user-related data are generated by users and verified by smart contracts on the blockchain.We implemented zkBID and built a blockchain test network for evaluation purposes.Our tests demonstrate the effectiveness of zkBID and suggest proper ways to configure zkBID system parameters.

Recommender systems play an important role in online platforms to provide personalized high-quality service for users. Sequential Recommendation (SR) captures users' dynamic preferences by modeling historical sequences, but most SR models suffer performance degradation due to data sparsity problem. To address this issue, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to transfer sequential patterns across domains to promote accuracy of single-domain recommendation services. Challenges arise when tackling CDSR, i.e., (1) how to extract intra- and inter-sequence collaborations within domains, (2) how to transfer stable interaction patterns across domains, and (3) how to alleviate data sparsity after integrating cross-domain knowledge. To this end, we proposeCHFMG, a contrastive hypergraph flow model with multifaceted gates, which contains two modules, i.e., dual hypergraph flow modeling and multi-view contrastive learning. The first module develops a dual hypergraph flow network to explore dynamic intra- and inter-domain sequential patterns. Innovative multifaceted attentive transfer gates connect local and global hypergraph flow, realizing internal feature fusion and external feature alignment in transfer. The second module achieves contrastive learning from two aspects, i.e., short-term contrasting for retaining sequential pattern consistency and long-term contrasting for enhancing distribution uniformity. Empirical studies on benchmark datasets demonstrate the effectiveness ofCHFMG.

Pre-trained language models acquire knowledge from vast amounts of text data, which can inadvertently contain sensitive information.To mitigate the presence of undesirable knowledge, the task of knowledge unlearning becomes crucial for language models.Previous research relies on gradient ascent methods to achieve knowledge unlearning, which is simple and effective.However, this approach calculates all the gradients of tokens in the sequence, potentially compromising the general ability of language models.To overcome this limitation, we propose an adaptive objective that calculates gradients with fine-grained control specifically targeting sensitive tokens.Our adaptive objective is pluggable, ensuring simplicity and enabling extension to the regularization-based framework that utilizes non-target data or other models to preserve general ability.Through extensive experiments targeting the removal of typical sensitive data, we demonstrate that our proposed method enhances the general ability of language models while achieving knowledge unlearning.Additionally, it demonstrates the capability to adapt to behavior alignment, eliminating all the undesirable knowledge within a specific domain.

Federated Recommendation (FedRec) is popularly investigated in personalized recommenders for preserving user privacy. However, due to the distributed training paradigm, FedRec is vulnerable to model poisoning attacks. In this paper, we focus on the targeted model poisoning attack against FedRec, which aims at effectively attacking the FedRec via uploading poisoned gradients to raise the exposure ratio of a multi-target item set. Previous attack methods excel with fewer target items but suffer performance decline as the amount of target items increases, which reveals two perennially neglected issues: (i) The simple promotion of prediction scores without considering intrinsic collaborations between users and items is ineffective in multi-target cases. (ii) Target items are heterogeneous, which requires discriminative attacking users and strategies for different targets. To address the issues, we propose a novel Heterogeneous Multi-target Transfer Attack framework named HMTA which consists of two stages, i.e., (1) diverse user agent generation and (2) optimal multi-target transport attack. The former stage leverages collaboration-aware manifold learning to extract latent associations among users and items, and develops a differentiable contrastive sorting to generate user agents from both difficulty and diversity scale. The latter stage conducts poisoning in a fine-grained and distinguishing way, which first completes distribution mapping from target items to generated user agents and then achieves a hybrid multi-target attack. Extensive experiments on benchmark datasets demonstrate the effectiveness of HMTA.

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aims to leverage time-evolving heterogeneous behavioral dependencies for better exploring users' potential intents on the target behavior. Solving MBSR is challenging. On the one hand, users exhibit diverse multi-behavior patterns due to personal characteristics. On the other hand, there exists comprehensive co-influence between behavior correlations and item collaborations, the intensity of which is deeply affected by temporal factors. To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance. PBAT includes two main modules, i.e, personalized behavior pattern generator and behavior-aware collaboration extractor. First, PBAT develops a personalized behavior pattern generator in the representation layer, which extracts dynamic and discriminative behavior patterns for sequential learning. Second, PBAT reforms the self-attention layer with a behavior-aware collaboration extractor, which introduces a fused behavior-aware attention mechanism for incorporating both behavioral and temporal impacts into collaborative transitions. We conduct experiments on three benchmark datasets and the results demonstrate the effectiveness and interpretability of our framework.