Disaggregated Memory Systems (DMS) hold substantial potential for cloud datacenters but face critical deployment barriers in multi-tenant RDMA environments. Existing DMS designs rely on idealized assumptions-overlooking interference from co-located RDMA applications, oversimplifying fabric topology considerations, and lacking elastic service-level objectives (SLOs) guarantees-resulting in performance degradation and resource inefficiency. To bridge these gaps, we introduce Wiseswap, an elastic, datacenter network-aware DMS that delivers robust memory disaggregation for multi-tenant clouds through three key innovations: (1) Preemption-enabled isolation: A low-overhead kernel mechanism utilizes WAIT/ENABLE RDMA primitives to prioritize latency-critical swap operations over user-space RDMA flows, maintaining tenant fairness; (2) Adaptive fabric path selection: In-kernel telemetry dynamically probes latency and routes memory traffic through uncongested paths, mitigating interference from elephant flows; (3) Feedback-directed autoscaling: Fine-grained optimization of DMS-specific parameters-dynamically optimizes resource allocation under fluctuating workloads, guaranteeing stringent SLOs while minimizing resource overhead. Evaluations demonstrate that Wiseswap improves throughput by 1.3-2.4x and reduces tail latency by 57-73% under contention compared to state-of-the-art solutions, while consistently meeting strict SLO targets.
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Opinion formation models are widely used to study social and behavioral processes on online social networks, yet their fairness remains largely unexplored. We study this novel problem in the context of the Friedkin–Johnsen model, a well-established framework for opinion dynamics. In this model, the expressed opinion of an individual evolves by combining peer opinions with a fixed inner opinion weighted by their stubbornness. We define a node's influence as the weight its inner opinion contributes to the public opinion. Given different groups of nodes, we require that influence is distributed fairly across groups. To achieve this, we design minimal interventions that adjust stubbornness, making individuals more receptive to others or more anchored to their own views. We derive closed-form expressions for how changes in the stubbornness of a single node affect influence and leverage them to develop efficient algorithms. Experiments on synthetic and real-world networks provide insights into the role of stubbornness in fairness and demonstrate the effectiveness and efficiency of our methods.
As a fundamental technique with many real-world applications, including social network analysis, center-based clustering may inadvertently discriminate against certain populations based on factors such as age, gender, or socioeconomic status, particularly when nodes are associated with sensitive attributes. In this work, we study the problem of fair k-center clustering in the streaming setting, which seeks to select representative items from a large data stream while respecting group-representation fairness. Given an input dataset in Euclidean space partitioned into m disjoint groups, the fairness constraint requires that the number of centers selected from each group satisfies a given upper bound. Moreover, the problem aims to select a set of centers that minimizes the maximum distance from any point to its nearest center (the k-center objective) while satisfying the fairness constraint. We present a one-pass streaming algorithm with approximation ratio 4.46, improving the previous best ratio of (5+?) for this problem in general metrics. Notably, our result establishes that streaming fair k-center admits a strictly better approximation ratio in Euclidean space than in general metrics, in contrast to the standard k-center problem, whose best-known approximation ratio is 2 in both Euclidean and general metric spaces. Finally, we complement our theoretical results with an empirical evaluation on five real-world social network datasets and million-scale synthetic datasets, demonstrating significant improvements over state-of-the-art methods in clustering quality while maintaining comparable runtime efficiency.
Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines.
Spamming activities (e.g., fake reviews, click farming, and deceptive content promotion) are increasingly conducted through collusive groups that exploit collective dynamics to manipulate platform metrics and mislead users, posing serious threats to the fairness, credibility, and functionality of online systems. To counteract these harmful behaviors, the task of spam detection has emerged as a critical area of research. However, existing detection methods generally remain limited in three key aspects: (i) They treat detection as a standard classification task, where representation learning and optimization are loosely coupled and suboptimal for capturing complex behaviors; (ii) They rely primarily on individual-level representation modeling, making it difficult to detect collective cheating strategies; (iii) They lack dedicated objective functions explicitly designed to characterize group-level spamming activities. To overcome these limitations, we introduce a collusion-aware Set-level learning framework (SetDet) that redefines the spam Detection task as a unified setwise optimization problem. Our approach offers three core advantages: (i) It enables end-to-end optimization by jointly learning representations and performing detection in a single, integrated process; (ii) It incorporates a model-level design for collusion representation, effectively capturing the temporal and relational patterns of coordinated spam; (iii) It pioneers a dedicated set-level optimization criterion that aligns closely with the structural characteristics of group-based cheating behaviors and accounts for class imbalance in real-world scenarios. Extensive experiments confirm the generalizability and superior performance of our framework across diverse spam scenarios and collusion strategies.
Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, current automated model and data card generation methods still face three key challenges: (i) Static templates. Most systems rely on fixed query templates that cannot adapt to diverse paper structures or evolving documentation requirements. (ii) Information scarcity. Web-scale repositories such as Hugging Face often provide incomplete or inconsistent metadata, resulting in missing or noisy information. (iii) Lack of benchmarks. The absence of standardized datasets and evaluation protocols prevents fair and reproducible assessment of documentation quality. To address these challenges, we propose AdaQE-CG, an Adaptive Query Expansion for Card Generation framework that integrates dynamic information extraction with cross-card knowledge transfer. The Intra-Paper Extraction via Context-Aware Query Expansion (IPE-QE) module iteratively refines extraction queries to capture richer and more complete information from scientific papers and repositories. The Inter-Card Completion using the MetaGAI Pool (ICC-MP) module enriches missing fields by transferring semantically relevant content from similar cards within a curated dataset. In addition, we construct MetaGAI-Bench, the first large-scale, expert-annotated benchmark for evaluating GAI documentation. Comprehensive experiments across five quality dimensions demonstrate that AdaQE-CG significantly outperforms existing approaches, surpasses human-authored data cards, and approaches human-level quality for model cards. Code, prompts, and data are publicly available at: https://github.com/haoxuan-unt2024/AdaQE-CG.
Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a matter of diversity enhancement or long-tail coverage, neglecting the deeper semantic layer that embodies the causal origins of the bias itself. Consequently, such shallow interpretations limit both their debiasing effectiveness and recommendation accuracy. In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). FairLRM decomposes popularity bias into item-side and user-side components, using structured instruction-based prompts to enhance the model's comprehension of both global item distributions and individual user preferences. Unlike traditional methods that rely on surface-level features such as ''diversity'' or ''debiasing'', FairLRM improves the model's ability to semantically interpret and address the underlying bias. Through empirical evaluation, we demonstrate that FairLRM enhances fairness and recommendation accuracy through a trustworthy, semantically grounded treatment of popularity bias. The source code is shown in https://github.com/LuoRenqiang/FairLRM.
Selfish mining attack is a well-known attack in blockchains where Byzantine validators collude to gain more revenue than their fair share. Prior studies show that selfish mining attack can be risk-free in the longest-chain variants of Proof-of-Stake (PoS) protocols, i.e., Byzantine validators will never be caught and do not take any risk of losing their revenue. However, it is still unclear whether risk-free selfish mining is possible for PoS in the non-global predictability model [EC 2019], where in the global unpredictability model, validators can predict the roles of all validators in advance. In this work, we study the nominated PoS protocol by Polkadot, a cryptocurrency with a market cap of over 7 billion USD (top 20 blockchains). Polkadot's PoS protocol falls into a hybrid predictability model (i.e., non-global predictability model) that has never been well studied before. We present the first two risk-free (and meanwhile profitable) selfish mining attacks against Polkadot's PoS protocol. In our attack, a mining pool with 33% Byzantine validators can launch risk-free selfish mining and earn 7.09% extra block rewards in expectation. Our experimental results using Polkadot's implementation show that our risk-free attacks achieve similar profit as conventional selfish mining attack, such as the well-known selfish mining against Bitcoin.
While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.
Privacy-preserving data trading allows data owners to sell data to consumers through a data trading web platform, the data market, without disclosing sensitive information in raw data. It enables legitimate data transmission and aggregation, facilitating large-scale data-driven model training. However, existing differential privacy-based approaches struggle to inject precisely calibrated noise in a trustworthy manner without revealing raw data to a third party, thus making them fail in achieving strong fairness and controllable privacy simultaneously, especially when facing malicious external adversaries or a corrupted data market. To address these issues, we present Xemis, a decentralized privacy-preserving data trading scheme, which builds upon a new multi-party computation (MPC)-based batched distributed noise sharing protocol, b-DNS. b-DNS facilitates the generation of threshold secret shares of Gaussian samples with low overhead and Byzantine robustness. Leveraging our designed game-theoretic mechanism and batched bits expansion mechanism, b-DNS achieves at least 12.7× faster and saves at least 43.6× bandwidth compared to ODO (EUROCRYPT'06) and CSU19 (CCS'19). Building atop b-DNS, Xemis further enables a decentralized, Byzantine robust data market to perturb the shares of blinded data under a controllable, precise noise level, without revealing the raw data or the perturbed data to the market. Additionally, Xemis utilizes a distributed demo dataset sampling-based mechanism and a Byzantine fault tolerance consensus-based method to enable fair value assessment and payment-data delivery. Consequently, Xemis achieves controllable privacy and strong fairness with Web3 compatibility under malicious market nodes, while achieving at least 3.3× faster than ZLM+24 (TIFS'24) with a 64-node market. The utility of noise-perturbed data is evaluated through image classification tasks on CIFAR-10, and when adding noise with privacy budget \privacyBudget=1 to 63% of the training data, the model maintains an accuracy of 83%.
Pre-trained Graph Neural Networks (GNNs) have demonstrated remarkable performance in graph mining tasks, yet they often amplify societal biases against protected demographic groups. Existing fairness-aware approaches primarily address discrimination based on a single sensitive attribute (e.g., gender or race), overlooking real-world scenarios where individuals possess multiple overlapping demographic characteristics, leading to unfair treatment of underrepresented subgroups. Moreover, incorporating extra fairness constraints into pre-trained GNNs usually requires full model retraining, which is computationally expensive and often impractical. To address these limitations, we propose a novel Multi-attribute Fairness-aware Prompt Tuning framework named MultiFPT. Our approach operates in two key stages: in the graph prompt learning stage, MultiFPT injects fairness-aware structural and feature prompts into pre-trained GNN inputs; in the adapter tuning stage, a lightweight adapter regularized by the Hilbert–Schmidt Independence Criterion (HSIC) enforces statistical independence between node representations and multiple sensitive attributes. Experiments on real-world datasets demonstrate that MultiFPT significantly improves multi-attribute fairness, reducing bias by approximately 30% on average in node classification while maintaining competitive predictive performance compared to state-of-the-art baselines.
Visual data from the Web power image classifiers, which underpin web services, including recommendation and moderation. However, Web data contain spurious correlations and social biases, and neural networks tend to learn biases in data. This can reinforce unfairness in web services and the web data, leading to a vicious cycle. In image classification, networks learn bias attributes for a specific class when most images contain the same attribute for a given class. Hence, training a fair and debiased classifier from a biased dataset demands handling imbalance between a majority of images with bias attributes (bias-aligned samples) and a minority without (bias-conflict samples). In this work, we introduce BiasEdit, a modular framework that automatically detects bias attributes from the original dataset and edits them to construct a debiased dataset. Specifically, BiasEdit first detects unknown bias attributes via statistical dependence and mutual information analysis of visual–linguistic representations, and then explicitly edits those attributes using text-guided image editing to generate realistic bias-conflict samples. Unlike prior works that assume known bias attributes or rely on synthetic mixing, our method operates without manual annotations and leverages off-the-shelf vision–language and editing models. BiasEdit addresses a fundamental challenge in Web-sourced visual AI, mitigating dataset-induced bias and achieving state-of-the-art debiasing performance even when training data are fully biased.
Graph Neural Networks fundamentally rely on homophily assumptions where connected nodes are expected to share similar labels, consequently suffering severe performance degradation in heterophilic graphs due to the indiscriminate neighbor aggregation mechanism. Although recent solutions have attempted to incorporate higher-order neighborhoods or reweighting schemes, they often inadvertently amplify structural noise by introducing a larger proportion of dissimilar nodes than similar ones, while simultaneously failing to capture nuanced contextual patterns due to their inability to discern subtle local structural variations across subgraphs. To holistically address these intractable and co-existing challenges, we propose the Contextual Structure Enhanced Selective Graph Convolutional Network (CSS-GCN), a novel architecture that organically synergizes contextual structure modeling with adaptive neighbor selection. Specifically, our approach employs ego-network partitioning and group fairness constraints to effectively quantify domain-invariant structural patterns, thereby countering the contextual blindness often observed in conventional GNNs. Complementarily, we design a selective propagation mechanism unifying adaptive neighborhood distribution-based similarity computation with the gated fusion of three distinct information pathways: potential homophilic neighbors identified through attribute-topology synergy, first-hop connections, and ego-representations. This dual-component framework enables nodes to dynamically filter out irrelevant signals while preserving structural consistency across diverse homophily-heterophily landscapes. Extensive validation on 10 real-world graphs demonstrates the effectiveness and superiority of our proposed approach.
We initiate the study of single-sample bilateral trade with a broker, drawing an analogy to the setting of single-sample bilateral trade without a broker considered in [1] and [5]. Our model captures the three-sided interaction in which a broker mediates trade between a buyer and seller, each described by a valuation distribution from which a single sample can be drawn. We consider two settings in particular: one where the valuation distributions of the buyer and seller are identical and one where the valuation distributions are stochastically ordered. We analyze simple mechanisms that rely only on a single sample from each agent's distribution and show that these mechanisms achieve constant-factor approximations to the first-best gains-from-trade (GFT), first-best social welfare (SW), and optimal profit under the standard monotone-hazard-rate assumption. We then complement these results with matching or nearly matching upper bounds on the GFT and SW of our mechanisms. Notably, in both settings, we observe fairly small losses in the approximation factors to the first-best GFT and first-best SW due to the existence of the broker (benchmarked against the corresponding approximation factors in the setting without a broker). Furthermore, our results stand in stark contrast to those of [12], who show inapproximability results under a strategic broker with full distributional knowledge. Our results provide insight into the design of data-efficient brokerage mechanisms for online marketplaces and decentralized trading platforms, where intermediaries must facilitate trade under severe informational constraints. They highlight how even minimal data can enable robust and incentive-compatible brokerage in uncertain markets for both the broker and the market participants.
In traditional resource/task allocation models, an agent's value on an item is fixed regardless of when, where, or how the agent consumes the item. In this work, we introduce a more general framework involving a set of positions (for example, roles within a company), where agents have valuations that depend on the specific positions they are assigned. This setting is well motivated as the agent faces different resources and support at different positions, and thus may need varying levels of effort to complete the item, supposing the items are chores. We further consider the constrained setting when each item can only be allocated to a certain set of positions (e.g., a research project cannot be assigned to an administrative staff). We particularly consider the case when the constraints form a graph where edges are items and vertices are positions, so an item can only be allocated to an incident position, which is known as the orientation problem [Christodoulou et al. EC 2023]. In this paper, fairness is measured by maximin share (MMS), and efficiency is measured by Pareto and social optimality. We present a complete set of results on the computational complexity and approximation algorithms for computing efficient and fair allocations.
We study the problem of fairly allocating m indivisible chores among n agents with additive cost functions. While the maximin share (MMS) is a prominent fairness criterion in theory, exact MMS allocations do not always exist. This has motivated relaxation that guarantees MMS fairness for only a subset of agents, aiming to maximize the number of satisfied agents. However, for chore allocation, guaranteeing most agents their full MMS is trivial but highly unsatisfactory, e.g., overburdening a single agent, which is undesirable in real-world platforms aiming for user retention and satisfaction. To address this, we propose a stronger and more practical notion called α-approximate all-but-one MMS (α-AMMS), which guarantees that n-1 agents receive their full MMS value, while the remaining agent receives an α-approximation. This model reflects common platform design goals, where satisfying the vast majority of users is critical, and near-fairness for the rest is acceptable. We show that there exist α-AMMS allocations, with α = 9/8 for three agents; α = 4/3 for four agents; and α = (n+1)2/4n for n ≥ 5 agents.
In a web-based review platform, papers from various research fields must be assigned to a group of reviewers. Each paper has an inherent cost, which represents the effort required for reading and evaluating it (e.g., the paper's length). Reviewers can bid on papers they are interested in, and if they are assigned a paper they have bid on, no cost is incurred. Otherwise, the inherent cost c(e) for paper e applies. We capture this with a model of restricted additive costs: every item e has a cost c(e), and each agent either incurs 0 or c(e) for e. In this work, we study how to allocate such chores fairly and efficiently. We propose an algorithm for computing allocations that are both EFX and MMS. Furthermore, we show that our algorithm achieves a 2-approximation of the optimal social cost, and the approximation ratio is optimal. We also show that slightly weaker fairness guarantees can be obtained if one requires the algorithm to run in polynomial time.
High-quality data labeling is essential for training robust machine learning models; however, existing methods often ignore fraud or assume non-negative worker utility, failing to penalize harmful contributions without discouraging participation. To address this, we propose the Negativity-Aware Incentive (NAI) mechanism which introduces two novel components. First, the Ability-Result Characteristic Function (AR-CF) adapts and extends Shapley value theory through signed valuation to explicitly capture both positive and negative contributions, by combining workers' abilities with real-time task results to define contribution values. Second, a dynamic stake pool mechanism employs pre-commitment economics with adaptive dual-control parameters to balance fairness and operational efficiency. Through extensive experiments on multimodal datasets (images, text, audio, video), NAI outperforms state-of-the-art baselines: it improves video labeling accuracy by 16.6%, and reduces fraudulent behaviors by 33.9%. Furthermore, our deployment on the NetEase Youling crowdsourcing platform, serving 430,000 registered workers with 80,000 monthly active workers, validates NAI's real-world viability. Real-time A/B testing shows a 59.6% improvement in labeling quality for beginner tasks and a consistent reduction in fraud rates (14.8%-33.9%) across difficulty levels. This work establishes a paradigm shift in crowdsourcing system design, demonstrating that explicit negative modeling can enhance data quality, optimize costs, and foster participation at scale.