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Adversarial Privacy Auditing of Synthetically Generated Data Produced by Large Language Models Using the TAPAS Toolbox.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Adversarial Privacy Auditing of Synthetically Generated Data Produced by Large Language Models Using the TAPAS Toolbox./
Author:	Dave, Krishna.
Description:	1 online resource (82 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-09.
Contained By:	Masters Abstracts International85-09.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798381959468

Adversarial Privacy Auditing of Synthetically Generated Data Produced by Large Language Models Using the TAPAS Toolbox.
Dave, Krishna.

Adversarial Privacy Auditing of Synthetically Generated Data Produced by Large Language Models Using the TAPAS Toolbox. - 1 online resource (82 pages)

Source: Masters Abstracts International, Volume: 85-09.

Thesis (M.S.)--University of California, Los Angeles, 2024.

Includes bibliographical references

In today's world with ever increasing need for data collection, there is a rise in demand for privacy-preserving synthetic data generation and privacy auditing techniques to safeguard sensitive user information and data from privacy attacks. This paper explores the adversarial privacy auditing of synthetically generated data produced by Large Language Models (LLMs) using the TAPAS "Toolbox for Adversarial Privacy Auditing of Synthetic Data" framework. This paper uses a healthcare dataset with sensitive user information of Breast Cancer to evaluate the privacy of the data using adversarial techniques. The paper compares and contrasts the data quality, data distributions and privacy-preserving metrics of the real dataset with synthetically generated datasets from several sources including LLMs such as the GReaT framework and OpenAI's GPT4, Generative Adversarial Networks (GANs), and an AI-generated dataset produced using a proprietary technique from an industry startup, mostly.ai.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381959468Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Adversarial techniquesIndex Terms--Genre/Form:

554714
Electronic books.

Adversarial Privacy Auditing of Synthetically Generated Data Produced by Large Language Models Using the TAPAS Toolbox.
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520 $a In today's world with ever increasing need for data collection, there is a rise in demand for privacy-preserving synthetic data generation and privacy auditing techniques to safeguard sensitive user information and data from privacy attacks. This paper explores the adversarial privacy auditing of synthetically generated data produced by Large Language Models (LLMs) using the TAPAS "Toolbox for Adversarial Privacy Auditing of Synthetic Data" framework. This paper uses a healthcare dataset with sensitive user information of Breast Cancer to evaluate the privacy of the data using adversarial techniques. The paper compares and contrasts the data quality, data distributions and privacy-preserving metrics of the real dataset with synthetically generated datasets from several sources including LLMs such as the GReaT framework and OpenAI's GPT4, Generative Adversarial Networks (GANs), and an AI-generated dataset produced using a proprietary technique from an industry startup, mostly.ai.
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653 $a Synthetically generated data
653 $a TAPAS toolbox
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