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Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications 被引量:1
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作者 Harshvardhan Aditya Siddansh Chawla +6 位作者 gunika dhingra Parijat Rai Saumil Sood Tanmay Singh Zeba Mohsin Wase Arshdeep Bahga Vijay K. Madisetti 《Journal of Software Engineering and Applications》 2024年第5期421-447,共27页
The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Infor... The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks. 展开更多
关键词 Large Language Models PII Leakage Privacy Memorization OVERFITTING Membership Inference Attack (MIA)
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Protecting LLMs against Privacy Attacks While Preserving Utility
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作者 gunika dhingra Saumil Sood +2 位作者 Zeba Mohsin Wase Arshdeep Bahga Vijay K. Madisetti 《Journal of Information Security》 2024年第4期448-473,共26页
The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Infor... The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. This inadvertent leakage of sensitive information typically occurs when the models are subjected to black-box attacks. To address the growing concerns of safeguarding private and sensitive information while simultaneously preserving its utility, we analyze the performance of Targeted Catastrophic Forgetting (TCF). TCF involves preserving targeted pieces of sensitive information within datasets through an iterative pipeline which significantly reduces the likelihood of such information being leaked or reproduced by the model during black-box attacks, such as the autocompletion attack in our case. The experiments conducted using TCF evidently demonstrate its capability to reduce the extraction of PII while still preserving the context and utility of the target application. 展开更多
关键词 Large Language Models PII Leakage PRIVACY Memorization Membership Inference Attack (MIA) DEFENSES Generative Adversarial Networks (GANs) Synthetic Data
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Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models
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作者 gunika dhingra Siddansh Chawla +1 位作者 Vijay K. Madisetti Arshdeep Bahga 《Journal of Software Engineering and Applications》 2024年第1期23-42,共20页
With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily meas... With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study. 展开更多
关键词 Large Language Models (LLMs) Smaller Language Models (SLMs) FINANCE NETWORKING Supervisor Model Scoring Function
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