Data Science and System Security

Read our publications from our Data Science & System Security researchers who aim to build novel big-data solutions and service platforms to simplify complex systems management. We develop new information technology that supports innovative applications, from big data analytics to the Internet of Things. Our experimental and theoretical research includes many data science and systems research domains including time series mining, deep learning, NLP and large language models, graph mining, signal processing, and cloud computing.

Posts

Incident Diagnosing and Reporting System based on Retrieval Augmented Large Language Model

/in /by

The Internet-of-Things (IoT) is widely used in many applications such as smart city, transportation, healthcare, and environment monitoring. A key task of IoT maintenance is to analyze the abnormal sensor records and generate incident report. Traditionally, domain experts engage in such labor intensive tasks. Recent advances in Large Language Model (LLM) have sparked interests in developing AI-based systems to automate these labor intensive processes. However, two critical problems hinder the effective application of LLM in IoTs: (1) LLM lacks background knowledge of deployed IoTs; and (2) the incidents are complex = events involving many sensors and components. LLM needs to understand the sensor relationships for accurate diagnosis. In this study, we propose a Retrieval Augmented language model based Incident Diagnosing and Reporting system (RAIDR) for IoT applications. RAIDR retrieves related system documents based on the incident features and leverages LLM to analyze anomalies, identify root causes, and automatically generate incident reports. The automated incident reporting process streamlines end users’ decision making for system maintenance and troubleshooting.

Read more

Improving Logits-based Detector without Logits from Black-box LLMs

/in /by

The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human-written text presents new challenges in distinguishing one from the other – a task further complicated by the frequent updates and closed nature of leading proprietary LLMs. Traditional logits-based detection methods leverage surrogate models for identifying LLM-generated content when the exact logits are unavailable from black-box LLMs. However, these methods grapple with the misalignment between the distributions of the surrogate and the often undisclosed target models, leading to performance degradation, particularly with the introduction of new, closed-source models. Furthermore, while current methodologies are generally effective when the source model is identified, they falter in scenarios where the model version remains unknown, or the test set comprises outputs from various source models. To address these limitations, we present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection even without logits from source LLMs. DALD is designed to align the surrogate model s distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations with minimal training investment. By leveraging corpus samples from publicly accessible outputs of advanced models such as ChatGPT, GPT-4, and Claude-3, DALD fine-tunes surrogate models to synchronize with unknown source model distributions effectively. Our approach performs SOTA in black-box settings on different advanced closed-source and open-source models. The versatility of our method enriches widely adopted zero-shot detection frameworks (DetectGPT, DNA-GPT, Fast-DetectGPT) with a plug-and-play enhancement feature. Extensive experiments validate that our methodology reliably secures high detection precision for LLM-generated text and effectively detects text from diverse model origins through a singular detector. Our method is also robust under the revised text attack and non-English texts.

Read more

Protecting Your LLMs with Information Bottleneck

/in /by

The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by jailbreaking attacks through optimized or manual adversarial prompts. To address this, we introduce the Information Bottleneck Protector (IBProtector), a defense mechanism grounded in the information bottleneck principle, and we modify the objective to avoid trivial solutions. The IBProtector selectively compresses and perturbs prompts, facilitated by a lightweight and trainable extractor, preserving only essential information for the target LLMs to respond with the expected answer. Moreover, we further consider a situation where the gradient is not visible to be compatible with any LLM. Our empirical evaluations show that IBProtector outperforms current defense methods in mitigating jailbreak attempts, without overly affecting response quality or inference speed. Its effectiveness and adaptability across various attack methods and target LLMs underscore the potential of IBProtector as a novel, transferable defense that bolsters the security of LLMs without requiring modifications to the underlying models.

Read more

A Survey on Detection of LLMs-Generated Content

/in /by

The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of sectors, including media, cybersecurity, public discourse, and education. As such, the ability to detect LLMs-generated content has become of paramount importance. We aim to provide a detailed overview of existing detection strategies and benchmarks, scrutinizing their differences and identifying key challenges and prospects in the field, advocating for more adaptable and robust models to enhance detection accuracy. We also posit the necessity for a multi-faceted approach to defend against various attacks to counter the rapidly advancing capabilities of LLMs. To the best of our knowledge, this work is the first comprehensive survey on the detection in the era of LLMs. We hope it will provide a broad understanding of the current landscape of LLMs-generated content detection, and we have maintained a website to consistently update the latest research as a guiding reference for researchers and practitioners.

Read more

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (EMNLP 2024)

/in /by

Large Language Models (LLMs) have achieved exceptional capabilities in open generation across various domains, yet they encounter difficulties with tasks that require intensive knowledge. To address these challenges, methods for integrating knowledge have been developed, which augment LLMs with domain-specific knowledge graphs through external modules. These approaches, however, face data inefficiency issues as they necessitate the processing of both known and unknown knowledge for fine-tuning. Thus, our research focuses on a novel problem: efficiently integrating unknown knowledge into LLMs without unnecessary overlap of known knowledge. A risk of introducing new knowledge is the potential forgetting of existing knowledge. To mitigate this risk, we propose the innovative InfuserKI framework. This framework employs transformer internal states to determine when to enrich LLM outputs with additional information, effectively preventing knowledge forgetting. Performance evaluations using the UMLS-2.5k and MetaQA domain knowledge graphs reveal that InfuserKI not only successfully integrates new knowledge but also outperforms state-of-the-art baselines, reducing knowledge forgetting by 9% and 6%, respectively.

Read more

Large Language Models Can Be Contextual Privacy Protection Learners

/in /by

The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless, such domain-specific fine-tuning data often contains contextually sensitive personally identifiable information (PII). Direct fine-tuning LLMs on this data without privacy protection poses a risk of data leakage of sensitive PII during inference time. To address this challenge, we introduce Contextual Privacy Protection Language Models (CPPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. Our work offers a theoretical analysis for model design and delves into various techniques such as corpus curation, penalty-based unlikelihood in training loss, and instruction-based tuning, etc. Extensive experiments across diverse datasets and scenarios demonstrate the effectiveness of our approaches. In particular, instruction tuning with both positive and negative examples, stands out as a promising method, effectively protecting private data while enhancing the model s knowledge. Our work underscores the potential for Large Language Models as robust contextual privacy protection learners.

Read more

The WizARd and Apprentice: An Augmented Reality Expert Capture System

/in /by

Learning to perform physical tasks is ubiquitous yet challenging without expert guidance. While Augmented Reality (AR) has been adopted to overlay instructions directly onto the physical context, the natural authoring of such content remains unexplored. To address this, we developed WizARd and Apprentice, an AR expert capture system for training novices using an AR headset. WizARd and Apprentice tracks and records expert demonstrations and moving objects, leveraging the natural synchronization of speech and action to identify key steps and automatically create spatial markers.

Read more

PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization

/in /by

Achieving carbon neutrality within industrial operations has become increasingly imperative for sustainable development. It is both a significant challenge and a key opportunity for operational optimization in industry 4.0. In recent years, Deep Reinforcement Learning (DRL) based methods offer promising enhancements for sequential optimization processes and can be used for reducing car-bon emissions. However, existing DRL methods need a pre-defined reward function to assess the impact of each action on the final sustainable development goals (SDG). In many real applications, such a reward function cannot be given in advance. To address the problem, this study proposes a Performance based Adversarial Imitation Learning (PAIL) engine. It is a novel method to acquire optimal operational policies for carbon neutrality without any pre-defined action rewards. Specifically, PAIL employs a Transformer-based policy generator to encode historical information and predict fol-lowing actions within a multi-dimensional space. The entire action sequence will be iteratively updated by an environmental simulator. Then PAIL uses a discriminator to minimize the discrepancy be-tween generated sequences and real-world samples of high SDG. In parallel, a Q-learning framework based performance estimator is de-signed to estimate the impact of each action on SDG. Based on these estimations, PAIL refines generated policies with the rewards from both discriminator and performance estimator. PAIL is evaluated on multiple real-world application cases and datasets. The experiment results demonstrate the effectiveness of PAIL comparing to other state-of-the-art baselines. In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.

Read more

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration (VLDB 2024)

/in /by

Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks. To alleviate this issue, knowledge integration methods have been proposed to enhance LLMs with domain-specific knowledge graphs using external modules. However, they suffer from data inefficiency as they require both known and unknown knowledge for fine-tuning. Thus, we study a novel problem of integrating unknown knowledge into LLMs efficiently without unnecessary overlap of known knowledge. Injecting new knowledge poses the risk of forgetting previously acquired knowledge. To tackle this, we propose a novel Infuser-Guided Knowledge Integration (InfuserKI) framework that utilizes transformer internal states to determine whether to enhance the original LLM output with additional information, thereby effectively mitigating knowledge forgetting. Evaluations on the UMLS-2.5k and MetaQA domain knowledge graphs demonstrate that InfuserKI can effectively acquire new knowledge and outperform state-of-the-art baselines by 9% and 6%, respectively, in reducing knowledge forgetting.

Read more

Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization

/in /by

In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the notable success, there is very limited literature that provides a theoretical tool for characterizing the behaviors of long-tail classes in graphs and gaining insight into generalization performance in real-world scenarios. To bridge this gap, we propose a generalization bound for long-tail classification on graphs by formulating the problem in the fashion of multi-task learning, i.e., each task corresponds to the prediction of one particular class. Our theoretical results show that the generalization performance of long-tail classification is dominated by the overall loss range and the task complexity. Building upon the theoretical findings, we propose a novel generic framework Hier-Tail for long-tail classification on graphs. In particular, we start with a hierarchical task grouping module that allows us to assign related tasks into hypertasks and thus control the complexity of the task space; then, we further design a balanced contrastive learning module to adaptively balance the gradients of both head and tail classes to control the loss range across all tasks in a unified fashion. Extensive experiments demonstrate the effectiveness of HierTail in characterizing long-tail classes on real graphs, which achieves up to 12.9% improvement over the leading baseline method in balanced accuracy.

Read more