Schema matching constitutes a pivotal phase in the data ingestion process for contemporary database systems. Its objective is to discern pairwise similarities between two sets of attributes, each associated with a distinct data table. This challenge emerges at the initial stages of data analytics, such as when incorporating a third-party table into existing databases to inform business insights. Given its significance in the realm of database systems, schema matching has been under investigation since the 2000s. This study revisits this foundational problem within the context of large language models. Adhering to increasingly stringent data security policies, our focus lies on the zero-shot and few-shot scenarios: the model should analyze only a minimal amount of customer data to execute the matching task, contrasting with the conventional approach of scrutinizing the entire data table. We emphasize that the zero-shot or few-shot assumption is imperative to safeguard the identity and privacy of customer data, even at the potential cost of accuracy. The capability to accurately match attributes under such stringent requirements distinguishes our work from previous literature in this domain.
论文检索
输入标题、作者或关键词，从 2,111 篇学术成果中精准定位
Precise crop yield predictions are of national importance for ensuring food security and sustainable agricultural practices. While AI-for-science approaches have exhibited promising achievements in solving many scientific problems such as drug discovery, precipitation nowcasting, etc., the development of deep learning models for predicting crop yields is constantly hindered by the lack of an open and large-scale deep learning-ready dataset with multiple modalities to accommodate sufficient information. To remedy this, we introduce the CropNet dataset, the first terabyte-sized, publicly available, and multi-modal dataset specifically targeting climate change-aware crop yield predictions for the contiguous United States (U.S.) continent at the county level. Our CropNet dataset is composed of three modalities of data, i.e., Sentinel-2 Imagery, WRF-HRRR Computed Dataset, and USDA Crop Dataset, for over 2200 U.S. counties spanning 6 years (2017-2022), expected to facilitate researchers in developing versatile deep learning models for timely and precisely predicting crop yields at the county-level, by accounting for the effects of both short-term growing season weather variations and long-term climate change on crop yields. Besides, we develop the CropNet package, offering three types of APIs, for facilitating researchers in downloading the CropNet data on the fly over the time and region of interest, and flexibly building their deep learning models for accurate crop yield predictions. Extensive experiments have been conducted on our CropNet dataset via employing various types of deep learning solutions, with the results validating the general applicability and the efficacy of the CropNet dataset in climate change-aware crop yield predictions. We have officially released our CropNet dataset on Hugging Face Datasets https://huggingface.co/datasets/CropNet/CropNet and our CropNet package on the Python Package Index (PyPI) https://pypi.org/project/cropnet. Code and tutorials are available at https://github.com/fudong03/CropNet.
This paper introduces FedSecurity, an end-to-end benchmark that serves as a supplementary component of the FedML library for simulating adversarial attacks and corresponding defense mechanisms in Federated Learning (FL). FedSecurity eliminates the need for implementing the fundamental FL procedures, e.g., FL training and data loading, from scratch, thus enables users to focus on developing their own attack and defense strategies. It contains two key components, including FedAttacker that conducts a variety of attacks during FL training, and FedDefender that implements defensive mechanisms to counteract these attacks. FedSecurity has the following features: i) It offers extensive customization options to accommodate a broad range of machine learning models (e.g., Logistic Regression, ResNet, and GAN) and FL optimizers (e.g., FedAVG, FedOPT, and FedNOVA); ii) it enables exploring the effectiveness of attacks and defenses across different datasets and models; and iii) it supports flexible configuration and customization through a configuration file and some APIs. We further demonstrate FedSecurity's utility and adaptability through federated training of Large Language Models (LLMs) to showcase its potential on a wide range of complex applications.
Once a very large payment processor client told us: 'if we are down for 5 minutes, we open the evening news - so don't screw up'. Processing billions of dollars per day, many financial institutions, need to continuously fight organized crime in the form of transaction fraud, stolen cards, anti-money laundering, account opening fraud, impersonations scams, phishing, and many other exotic and ever changing attacks from organized crime groups worldwide. In fact, it is estimated that in 2023 the global losses in fraud scams and bank fraud reached 485.6 billion. However, in addition to having very good detection rates and very low false positive rates, financial institutions also need to maintain very high availability rates, very low latencies, very high throughputs, automatic fault tolerance, auto scale up and down, and more. In this talk we cover some lessons related to running ML models in harsh, mission critical environments. We describe data issues, scale issues, ethical issues, system issues, security issues, compliance issues, business and regulation issues, and some architectural tradeoffs and architectural evolutions.
Logs are ubiquitous digital footprints, playing an indispensable role in system diagnostics, security analysis, and performance optimization. The extraction of actionable insights from logs is critically dependent on the log parsing process, which converts raw logs into structured formats for downstream analysis. Yet, the complexities of contemporary systems and the dynamic nature of logs pose significant challenges to existing automatic parsing techniques. The emergence of Large Language Models (LLM) offers new horizons. With their expansive knowledge and contextual prowess, LLMs have been transformative across diverse applications. Building on this, we introduce LogParser-LLM, a novel log parser integrated with LLM capabilities. This union seamlessly blends semantic insights with statistical nuances, obviating the need for hyper-parameter tuning and labeled training data, while ensuring rapid adaptability through online parsing. Further deepening our exploration, we address the intricate challenge of parsing granularity, proposing a new metric and integrating human interactions to allow users to calibrate granularity to their specific needs. Our method's efficacy is empirically demonstrated through evaluations on the Loghub-2k and the large-scale LogPub benchmark. In evaluations on the LogPub benchmark, involving an average of 3.6 million logs per dataset across 14 datasets, our LogParser-LLM requires only 272.5 LLM invocations on average, achieving a 90.6% F1 score for grouping accuracy and an 81.1% for parsing accuracy. These results demonstrate the method's high efficiency and accuracy, outperforming current state-of-the-art log parsers, including pattern-based, neural network-based, and existing LLM-enhanced approaches.
Maritime traffic management increasingly relies on vessel position information provided by terrestrial and satellite networks of the Automatic Identification System (AIS). Unfortunately, the problem of missing AIS data can lead to long-term gaps in vessel trajectory, raising corresponding security concerns regarding collision risks and illicit activities. Existing imputation approaches are often constrained by vehicle-based low-sampling trajectories, hindering their ability to address unique characteristics of maritime transportation systems and long-term missing scenarios. To tackle these challenges, we propose a novel generative framework for long-term vessel trajectory imputation. Our framework considers irregular tracks of vessels, which differ from those of cars due to the absence of a structured road network, and ensures the continuity of multi-point imputed trajectories. Specifically, we first utilize a pre-trained trajectory embedding block to capture patterns of vessel movements. Subsequently, we introduce a diffusion-based model for generating missing trajectories, where observed trajectory modeling with transformer encoding architecture and embeddings of both historical vessel trajectory and external factors serve as conditional information. In particular, we design a physics-guided discriminator in the training stage, which imposes kinematic constraints between locations and angles to improve the continuity of the imputed trajectories. Comprehensive experiments and analysis on a real-world AIS dataset confirm the effectiveness of our proposed approach.
Vertical Federated Learning (VFL) has garnered significant attention due to its applicability in multi-party collaborative learning and the increasing demand for privacy-preserving measures. Most existing VFL algorithms primarily focus on accuracy as the training model metric. However, the data we access is often imbalanced in the real world, making it difficult for models based on accuracy to correctly classify minority samples. The Area Under the Curve (AUC) serves as an effective metric to evaluate the performance of a model on imbalanced data. Therefore, optimizing AUC can enhance the model's ability to handle imbalanced data. Besides, computational resources within VFL systems are also imbalanced, which makes synchronous VFL algorithms are difficult to apply in the real world. To address the double imbalance issue, we propose Asynchronous Vertical Federated Kernelized AUC Maximization (AVFKAM). Specifically, AVFKAM asynchronously updates a kernel model based on triply stochastic gradients with respect to (w.r.t.) the pairwise loss and random feature approximation. To facilitate theoretical analysis, we transfer the asynchrony of model coefficients to the functional gradient through a dual relationship between coefficients and objective function. Furthermore, we demonstrate that AVFKAM converges to the optimal solution at a rate of O(1/t), where t represents the global iteration number, and discuss the security of the model. If t is denoted as the global iteration number, we provide that it converges to the optimal solution with the rate of O(1/t). Finally, experimental results on various benchmark datasets demonstrate that AVFKAM maintains high AUC performance and efficiency.
Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.
Make Your Home Safe: Time-aware Unsupervised User Behavior Anomaly Detection in Smart Homes via Loss-guided Mask
PDF ↗Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments demonstrate that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.
Crime risk map plays a crucial role in urban planning and public security management. Traditionally, it is obtained solely from historical crime incidents or inferred from limited environmental factors, which are not sufficient to accurately model the occurrences of crimes over the geographical space well. Motivated by the impressive and realistic conditional generating power of diffusion models, in this paper, we propose a multimodal conditional diffusion method, namely, DiffCrime, to infer the crime risk map based on datasets in various domains, i.e., historical crime incidents, satellite imagery, and map imagery. It is equipped with a history-gated multimodal denoising network, i.e., HamNet, dedicated to the crime risk map inference. HamNet emphasizes the importance of historical crime data via a Gated-based History Fusion (GHF) module and adaptively controls multimodal conditions to be fused across different diffusion time steps via a Time step-Aware Modality Fusion (TAMF) module. Extensive experiments on two real-world datasets demonstrate the effectiveness of DiffCrime, which outperforms baselines by at least 43% and 31% in terms of RMSE, respectively.
Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attention. Despite the impressive progress, the research question regarding the safety vulnerability of LLM-empowered RecSys still remains largely under-investigated. Given the security and privacy concerns, it is more practical to focus on attacking the black-box RecSys, where attackers can only observe the system's inputs and outputs. However, traditional attack approaches employing reinforcement learning (RL) agents are not effective for attacking LLM-empowered RecSys due to the limited capabilities in processing complex textual inputs, planning, and reasoning. On the other hand, LLMs provide unprecedented opportunities to serve as attack agents to attack RecSys because of their impressive capability in simulating human-like decision-making processes. Therefore, in this paper, we propose a novel attack framework called CheatAgent by harnessing the human-like capabilities of LLMs, where an LLM-based agent is developed to attack LLM-Empowered RecSys. Specifically, our method first identifies the insertion position for maximum impact with minimal input modification. After that, the LLM agent is designed to generate adversarial perturbations to insert at target positions. To further improve the quality of generated perturbations, we utilize the prompt tuning technique to improve attacking strategies via feedback from the victim RecSys iteratively. Extensive experiments across three real-world datasets demonstrate the effectiveness of our proposed attacking method.
Preserving individual privacy while enabling collaborative data sharing is crucial for organizations. Synthetic data generation is one solution, producing artificial data that mirrors the statistical properties of private data. While numerous techniques have been devised under differential privacy, they predominantly assume data is centralized. However, data is often distributed across multiple clients in a federated manner. In this work, we initiate the study of federated synthetic tabular data generation. Building upon a SOTA central method known as AIM, we present DistAIM and FLAIM. We first show that it is straightforward to distribute AIM, extending a recent approach based on secure multi-party computation which necessitates additional overhead, making it less suited to federated scenarios. We then demonstrate that naively federating AIM can lead to substantial degradation in utility under the presence of heterogeneity. To mitigate both issues, we propose an augmented FLAIM approach that maintains a private proxy of heterogeneity. We simulate our methods across a range of benchmark datasets under different degrees of heterogeneity and show we can improve utility while reducing overhead.
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning. While GPL offers superior effectiveness in graph knowledge transfer and computational efficiency, the security risks posed by backdoor poisoning effects embedded in pretrained models remain largely unexplored. Our study provides a comprehensive analysis of GPL's vulnerability to backdoor attacks. We introduce CrossBA, the first cross-context backdoor attack against GPL, which manipulates only the pretraining phase without requiring knowledge of downstream applications. Our investigation reveals both theoretically and empirically that tuning trigger graphs, combined with prompt transformations, can seamlessly transfer the backdoor threat from pretrained encoders to downstream applications.Through extensive experiments involving 3 representative GPL methods across 5 distinct cross-context scenarios and 5 benchmark datasets of node and graph classification tasks, we demonstrate that CrossBA consistently achieves high attack success rates while preserving the functionality of downstream applications over clean input. We also explore potential countermeasures against CrossBA and conclude that current defenses are insufficient to mitigate CrossBA. Our study highlights the persistent backdoor threats to GPL systems, raising trustworthiness concerns in the practices of GPL techniques.
Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
PDF ↗Job recommender systems are crucial for aligning job opportunities with job-seekers in online job-seeking. However, users tend to adjust their job preferences to secure employment opportunities continually, which limits the performance of job recommendations. The inherent frequency of preference drift poses a challenge to promptly and precisely capture user preferences. To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information. Specifically, BISTRO is composed of three stages: 1) coarse-grained semantic clustering, 2) fine-grained job preference extraction, and 3) personalized top-k job recommendation. Initially, BISTRO segments the user interaction sequence into sessions and leverages session-based semantic clustering to achieve broad identification of person-job matching. Subsequently, we design a hypergraph wavelet learning method to capture the nuanced job preference drift. To mitigate the effect of noise in interactions caused by frequent preference drift, we innovatively propose an adaptive wavelet filtering technique to remove noisy interaction. Finally, a recurrent neural network is utilized to analyze session-based interaction for inferring personalized preferences. Extensive experiments on three real-world offline recruitment datasets demonstrate the significant performances of our framework. Significantly, BISTRO also excels in online experiments, affirming its effectiveness in live recruitment settings. This dual success underscores the robustness and adaptability of BISTRO. The source code is available at https://github.com/Applied-Machine-Learning-Lab/BISTRO.
The development of the smart manufacturing trend includes the integration of Artificial Intelligence technologies into industrial processes. One example of such implementation is deep learning models that diagnose the current state of a technological process. Recent studies have demonstrated that small data perturbations, named adversarial attacks, can significantly affect the correct predictions of such models. This fact is critical in industrial systems, where AI-based decisions can be made to manage physical equipment. In this work, we present a system which can help to evaluate the robustness of technological process diagnosis models to adversarial attacks, as well as consider protection options. We briefly review the system's modules and also consider some useful applications. Our demo video is available at: http://tinyurl.com/3by9zcj5
We introduce an open-source platform for annotating body-worn video (BWV) footage aimed at enhancing transparency and accountability in policing. Despite the widespread adoption of BWVs in police departments, analyzing the vast amount of footage generated has presented significant challenges. This is primarily due to resource constraints, the sensitive nature of the data, which limits widespread access, and consequently, lack of annotations for training machine learning models. Our platform, called CVAT-BWV, offers a secure, locally hosted annotation environment that integrates several AI tools to assist in annotating multimodal data. With features such as automatic speech recognition, speaker diarization, object detection, and face recognition, CVAT-BWV aims to reduce the manual annotation workload, improve annotation quality, and allow for capturing perspectives from a diverse population of annotators. This tool aims to streamline the collection of annotations and the building of models, enhancing the use of BWV data for oversight and learning purposes to uncover insights into police-civilian interactions.
Vertical federated learning (VFL) is a distributed machine learning paradigm that collaboratively trains models using passive parties with features and an active party with additional labels. While VFL offers privacy preservation through data localization, the threat of label leakage remains a significant challenge. Label leakage occurs due to label inference attacks, where passive parties attempt to infer labels for their privacy and commercial value. Extensive research has been conducted on this specific VFL attack, but a comprehensive summary is still lacking. To bridge this gap, our paper aims to survey the existing label inference attacks and defenses. We propose two new taxonomies for both label inference attacks and defenses, respectively. Beyond summarizing the current state of research, we highlight techniques that we believe hold potential and could significantly influence future studies. Moreover, experimental benchmark datasets and evaluation metrics are summarized to provide a guideline for subsequent work.
Given a directed graph, a set of source nodes, a target node and a budget, we study the problem of maximizing the number of source nodes disconnected from the target node by removing edges not exceeding the budget. Our model is mainly motivated by a cyber security use case where we need to minimize the attack surface of a Windows Active Directory system. In these high-profile attacks, the attackers first compromise a source (i.e., a compromised user node) and then laterally move to a destination (i.e., a high-privileged admin node). Our aim is to minimize the number of users with a path to the admin. We first prove that the problem is NP-hard. Algorithms for exact optimality usually struggle to converge on graphs that approach real-world network scales and therefore are not practical for usage. In light of this, we study anytime algorithms that return an acceptable result whenever the algorithm is terminated, and can improve optimality by allowing longer computational time. We observe the source connectivity of directed graphs, based on which we propose a novel anytime algorithm---the spiral algorithm. We also develop two Monte Carlo Tree Search (MCTS) algorithms as a baseline to study the performance of typical anytime algorithms for our problem, and show that the spiral algorithm improves the optimality at a significantly faster speed and therefore exhibits better anytime behavior compared with MCTS.
Due to the potential leakage of sensitive information in text, there is a societal call for feeding privacy-preserving text to model training. Recently, a lot of work showed that using synthetic text with differential privacy, rather than private text, can provide a strong privacy protection. However, achieving higher semantic similarity between synthetic and private text has not been thoroughly investigated. In this paper, we propose an approach that combines the iteratively optimized mindset from genetic algorithms to align the distribution of synthetic text with that of private text. Furthermore, not only does the final synthetic text meet the requirements of privacy protection, but also has a high level of quality. Through comparisons with various baselines on different datasets, we demonstrate that our synthetic text can closely match the utility of private text, while providing privacy protection standards robust enough to resist membership inference attacks from malicious users.
HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis
PDF ↗Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effective paradigm for privacy preservation in multimodal tasks. However, they often overlook the privacy distinctions among different modalities, struggling to strike a balance between performance and privacy preservation. Consequently, it poses an intriguing question of maximizing multimodal utilization to improve performance while simultaneously protecting necessary modalities. This paper forms the first attempt at modality-specified (i.e., audio and visual) privacy preservation in MSA tasks. We propose a novel Hybrid Distributed cross-modality cGAN framework (HyDiscGAN), which learns multimodality alignment to generate fake audio and visual features conditioned on shareable de-identified textual data. The objective is to leverage the fake features to approximate real audio and visual content to guarantee privacy preservation while effectively enhancing performance. Extensive experiments show that compared with the state-of-the-art MSA model, HyDiscGAN can achieve superior or competitive performance while preserving privacy.