Are language models leaking personal information? Memorization vs. Association

Shao, Hanyin

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https://hdl.handle.net/2142/120435 Copy

Description

Title

Are language models leaking personal information? Memorization vs. Association

Author(s)

Shao, Hanyin

Issue Date

2023-05-02

Director of Research (if dissertation) or Advisor (if thesis)

Chang, Kevin Chen-Chuan

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Natural Language Processing
• NLP

Abstract

Large pre-trained language models (PLMs) have transformed the field of natural language processing (NLP) in recent years. PLMs have become the basis for various state-of-the-art NLP systems. Despite the great success of PLMs solving a wide range of NLP tasks, there is rising concern about privacy risks brought with PLMs. For example, recent studies show that PLMs memorize a great portion of training data, including sensitive information, while the information may be leaked unintentionally and utilized by malicious adversaries. In this thesis, we evaluate whether PLMs are prone to leaking personal information and discuss possible reasons behind the privacy leakage. Specifically, we attempt to query PLMs for a target email address with contexts of the email address or prompts containing the owner’s name. We find that PLMs do leak personal information mainly due to memorization. However, the risk of specific personal information being extracted by attackers is low because the models are weak at associating personal identifying information with its owner. We also try to quantify PLMs’ capability of association to help validate the safety of PLM in terms of privacy preserving.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120435

Copyright and License Information

Copyright 2023 Hanyin Shao

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