The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts

Zhili Shen, Zihang Xi, Ying He, Wei Tong, Jingyu Hua, Sheng Zhong·June 20, 2024

Summary

The paper presents Prompt Privacy Sanitizer (ProSan), an end-to-end framework designed to protect privacy in online chatbot prompts while maintaining usability. ProSan addresses privacy concerns by anonymizing prompts, adjusting protection based on word importance and privacy risk, and adapting to different computational resources. It outperforms baselines like HaS and SanText+ in terms of privacy hiding rates (PHR) and task performance, with minimal usability impact. ProSan evaluates word importance using LLMs, dynamically adjusts anonymity, and offers a lightweight option for resource-constrained users. The study compares ProSan to other methods, highlighting its ability to balance privacy and usability across tasks like question answering, text summarization, and code generation. Future work will explore further privacy methods and broader applications.

Key findings

7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of privacy concerns related to online chatbots, specifically focusing on the inadvertent exposure of sensitive information in prompts to large language models (LLMs) . This problem is not entirely new, as previous works have attempted solutions based on local deployment, embedding perturbation, and homomorphic encryption, but these methods have limitations in terms of usability, computational costs, and system modifications . The paper introduces a novel framework called Prompt Privacy Sanitizer (ProSan) to produce anonymized prompts that remove contextual privacy while maintaining task usability and human readability, thereby offering a more effective solution to the privacy challenges associated with online prompt-based LLM applications .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to privacy protection in online prompt-based large language model (LLM) applications. It introduces the Prompt Privacy Sanitizer (ProSan), an end-to-end framework designed to produce anonymized prompts by removing contextual privacy while ensuring task usability and human readability . The hypothesis revolves around addressing privacy concerns associated with prompts inadvertently containing sensitive information exposed to LLMs, and the need for effective privacy protection mechanisms in online LLM services . The study focuses on achieving high usability and dynamic anonymity by adjusting protection targets and strength based on word importance and privacy leakage risks in prompts, catering to diverse computational resource conditions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" proposes several innovative ideas, methods, and models related to privacy and utility in text data processing . Here are some key points from the paper:

  1. Sanitizing Sentence Embeddings for Local Differential Privacy: The paper introduces a method for sanitizing sentence embeddings and labels to ensure local differential privacy, which is crucial for protecting sensitive information in text data .

  2. Sentence-Level Privacy for Document Embeddings: It discusses a technique for ensuring privacy at the sentence level in document embeddings, which is essential for maintaining confidentiality in text data processing .

  3. Privacy-and Utility-Preserving Textual Analysis: The paper presents a model that focuses on preserving privacy and utility in textual analysis through calibrated multivariate perturbations, offering a balance between data protection and data usability .

  4. Auto-Encoder Based Differentially Private Text Transformation: It introduces a method called Adept, which is based on auto-encoder for achieving differentially private text transformation, enhancing privacy in text data processing .

  5. Anonymization Models for Text Data: The paper explores anonymization models for text data, addressing the challenges and future directions in anonymizing text to prevent privacy disclosure by removing personally identifiable information (PII) .

  6. Prompt Privacy Protection: It discusses a lightweight framework called Hide and Seek (HAS) for prompt privacy protection, which is designed to enhance privacy in text data processing .

  7. Language Model Privacy: The paper delves into methods like natural language understanding with privacy-preserving BERT and differentially private prompt learning for large language models to ensure privacy in language processing tasks .

These innovative ideas, methods, and models proposed in the paper contribute significantly to the field of privacy preservation and utility enhancement in text data processing, offering valuable insights into safeguarding sensitive information while maintaining data usability and effectiveness. The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" introduces the Prompt Privacy Sanitizer (ProSan) framework, which offers several key characteristics and advantages compared to previous methods in the field of privacy protection in text data processing .

  1. Dynamic Anonymity and Usability Balance: ProSan dynamically adjusts its protection targets and strength based on the importance of words and the privacy leakage risk of prompts, ensuring a balance between usability and anonymity .

  2. Seamless Integration into Online LLM Service Pipeline: ProSan can be seamlessly integrated into the online Large Language Model (LLM) service pipeline, allowing for efficient and effective privacy protection in prompt-based LLM applications without excessive system modifications .

  3. Adaptability to Computational Resource Conditions: ProSan is capable of adapting to diverse computational resource conditions, ensuring privacy protection even for mobile devices with limited computing power, addressing the challenge of resource constraints in privacy preservation .

  4. Automated Prompt Anonymization Scheme: The framework includes an automated prompt anonymization scheme that generates massive original prompt-anonymized prompt pairs as training data, reducing the computational burden on users and enhancing adaptivity .

  5. Effective Removal of Private Information: ProSan effectively removes private information across various tasks such as question answering, text summarization, and code generation, with minimal reduction in task performance, showcasing its effectiveness in privacy protection .

  6. Enhanced Task Performance: Despite focusing on privacy protection, ProSan maintains task performance by effectively balancing the removal of private information with task usability, ensuring that the anonymized prompts remain usable and maintain task integrity .

  7. Superior Precision and Recall: ProSan outperforms other methods in distinguishing names with varying importance, ensuring superior precision, recall, and F1 scores for anonymization tasks related to name privacy attributes, highlighting its effectiveness in preserving usability while protecting privacy .

Overall, the characteristics and advantages of the ProSan framework, as detailed in the paper, demonstrate its effectiveness in achieving dynamic anonymity, seamless integration, adaptability to resource constraints, automated prompt anonymization, task performance maintenance, and superior precision and recall in privacy protection tasks compared to previous methods in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of balancing usability and privacy in prompts. Noteworthy researchers in this area include C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, D. Yang , J. Devlin, M.-W. Chang, K. Lee, K. Toutanova , B. Lester, R. Al-Rfou, N. Constant , A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi`ere, N. Goyal, E. Hambro, F. Azhar et al. , M. Du, X. Yue, S. S. Chow, H. Sun , C. Meehan, K. Mrini, K. Chaudhuri , O. Feyisetan, B. Balle, T. Drake, T. Diethe , S. Krishna, R. Gupta, C. Dupuy , Y. Chen, T. Li, H. Liu, Y. Yu , S. Utpala, S. Hooker, P.-Y. Chen , A. Zou, Z. Wang, J. Z. Kolter, M. Fredrikson , and many more.

The key to the solution mentioned in the paper is the "Prompt Privacy Sanitizer" (ProSan), which is an end-to-end prompt privacy protection framework. ProSan aims to produce anonymized prompts by removing contextual privacy while ensuring task usability and human readability. It can be seamlessly integrated into the online Large Language Model (LLM) service pipeline. ProSan adjusts its protection targets and strength based on word importance and privacy leakage risk in prompts, ensuring high usability and dynamic anonymity. Additionally, ProSan can adapt to diverse computational resource conditions, making it suitable for privacy protection even on mobile devices with limited computing power .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the ProSan framework in protecting privacy in user prompts submitted to online large language models (LLMs) . The ProSan framework aims to produce anonymized prompts by dynamically balancing usability and anonymity, considering the importance of words and the privacy leakage risk of the prompts . The experiments demonstrated that ProSan effectively removes private information across various tasks, such as question answering, text summarization, and code generation, with minimal reduction in task performance .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MedQA dataset, which includes multiple-choice questions related to medical information . The code used in the study is not explicitly mentioned as open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces Prompt Privacy Sanitizer (ProSan), a framework designed to protect privacy in online chatbot applications by anonymizing prompts while maintaining usability and readability . The experiments conducted demonstrate that ProSan effectively removes private information across various tasks like question answering, text summarization, and code generation with minimal impact on task performance . The results show that ProSan adjusts its protection targets and strength based on the importance of words and the privacy leakage risk of prompts, ensuring high usability and dynamic anonymity .

Furthermore, the experiments reveal that ProSan's usability remains high, with only a minimal decrease in accuracy ranging from 0.4% to 4.6%, even for resource-limited versions of the framework . In contrast, other methods like HaS and SanText+ exhibit significant drops in accuracy, up to 39.6%, due to overly broad or rigid protection goals . This comparison highlights the effectiveness of ProSan in balancing usability and privacy protection, supporting the scientific hypotheses put forth in the paper .


What are the contributions of this paper?

The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" introduces the Prompt Privacy Sanitizer (ProSan), which is an end-to-end framework designed to protect privacy in prompts while maintaining usability and human readability . The key contributions of this paper include:

  • Introducing ProSan, a framework that produces anonymized prompts by removing contextual privacy, ensuring task usability, and seamlessly integrating into online large language model (LLM) services .
  • Flexibly adjusting protection targets and strength based on word importance and privacy leakage risk in prompts, ensuring high usability and dynamic anonymity .
  • Demonstrating the effectiveness of ProSan in removing private information across various tasks like question answering, text summarization, and code generation with minimal impact on task performance .

What work can be continued in depth?

Further work that can be continued in depth includes:

  • Exploring the effectiveness of text privacy protection by developing objective metrics for evaluating the anonymization of prompts .
  • Investigating the impact of privacy leakage in prompts on achieving secure general artificial intelligence, especially in the field of natural language processing .
  • Conducting research on privacy attacks on language models, such as multi-step jailbreaking attacks, to enhance the security of online prompt-based LLM applications .
  • Developing frameworks and methodologies for protecting user privacy in remote conversational systems through privacy-preserving text sanitization techniques .
  • Exploring model compression and acceleration techniques for pretrained language models to enhance inference efficiency and reduce computational burden .
  • Continuing research on privacy requirements engineering, named entity recognition-based privacy approaches, and machine learning systems for redacting documents to address privacy concerns in text data .

Tables

2

Introduction
Background
Evolution of online chatbots and privacy concerns
Importance of privacy in conversational AI
Objective
To develop an effective and usable privacy protection solution for chatbot prompts
Address privacy risks while maintaining task performance
Method
Data Collection
Selection of chatbot prompts and datasets
Gathering ground truth privacy risks and task performance data
Data Preprocessing
Cleaning and standardizing chatbot prompts
Identifying sensitive and non-sensitive words
ProSan Components
Anonymization
Word-level anonymization techniques
LLM-based word importance assessment
Dynamic Protection
Adaptive privacy adjustment based on word importance
Risk-based anonymity levels
Resource Optimization
Lightweight version for resource-constrained environments
Evaluation Metrics
Privacy Hiding Rates (PHR)
Task Performance Metrics (accuracy, usability)
Baseline Comparison
HaS (Hierarchical Sanitization) and SanText+ comparison
Performance analysis in various tasks (QA, summarization, code generation)
Results and Evaluation
ProSan's PHR and task performance
Usability impact analysis
Advantages over baseline methods
Discussion
Comparison with existing privacy solutions
Trade-offs between privacy and usability
Limitations and future improvements
Future Work
Exploration of advanced privacy techniques
Expanding ProSan's applicability to diverse chatbot scenarios
Conclusion
Summary of ProSan's contributions
Implications for privacy-conscious chatbot development
Recommendations for future research directions
Basic info
papers
cryptography and security
computation and language
artificial intelligence
Advanced features
Insights
What is the primary purpose of Prompt Privacy Sanitizer (ProSan)?
What methods does ProSan use to evaluate word importance and adjust anonymity?
How does ProSan address privacy concerns in online chatbot prompts?
How does ProSan compare to HaS and SanText+ in terms of privacy and task performance?

The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts

Zhili Shen, Zihang Xi, Ying He, Wei Tong, Jingyu Hua, Sheng Zhong·June 20, 2024

Summary

The paper presents Prompt Privacy Sanitizer (ProSan), an end-to-end framework designed to protect privacy in online chatbot prompts while maintaining usability. ProSan addresses privacy concerns by anonymizing prompts, adjusting protection based on word importance and privacy risk, and adapting to different computational resources. It outperforms baselines like HaS and SanText+ in terms of privacy hiding rates (PHR) and task performance, with minimal usability impact. ProSan evaluates word importance using LLMs, dynamically adjusts anonymity, and offers a lightweight option for resource-constrained users. The study compares ProSan to other methods, highlighting its ability to balance privacy and usability across tasks like question answering, text summarization, and code generation. Future work will explore further privacy methods and broader applications.
Mind map
Task Performance Metrics (accuracy, usability)
Privacy Hiding Rates (PHR)
Risk-based anonymity levels
Adaptive privacy adjustment based on word importance
LLM-based word importance assessment
Word-level anonymization techniques
Evaluation Metrics
Lightweight version for resource-constrained environments
Resource Optimization
Dynamic Protection
Anonymization
Expanding ProSan's applicability to diverse chatbot scenarios
Exploration of advanced privacy techniques
Performance analysis in various tasks (QA, summarization, code generation)
HaS (Hierarchical Sanitization) and SanText+ comparison
ProSan Components
Gathering ground truth privacy risks and task performance data
Selection of chatbot prompts and datasets
Address privacy risks while maintaining task performance
To develop an effective and usable privacy protection solution for chatbot prompts
Importance of privacy in conversational AI
Evolution of online chatbots and privacy concerns
Recommendations for future research directions
Implications for privacy-conscious chatbot development
Summary of ProSan's contributions
Future Work
Advantages over baseline methods
Usability impact analysis
ProSan's PHR and task performance
Baseline Comparison
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results and Evaluation
Method
Introduction
Outline
Introduction
Background
Evolution of online chatbots and privacy concerns
Importance of privacy in conversational AI
Objective
To develop an effective and usable privacy protection solution for chatbot prompts
Address privacy risks while maintaining task performance
Method
Data Collection
Selection of chatbot prompts and datasets
Gathering ground truth privacy risks and task performance data
Data Preprocessing
Cleaning and standardizing chatbot prompts
Identifying sensitive and non-sensitive words
ProSan Components
Anonymization
Word-level anonymization techniques
LLM-based word importance assessment
Dynamic Protection
Adaptive privacy adjustment based on word importance
Risk-based anonymity levels
Resource Optimization
Lightweight version for resource-constrained environments
Evaluation Metrics
Privacy Hiding Rates (PHR)
Task Performance Metrics (accuracy, usability)
Baseline Comparison
HaS (Hierarchical Sanitization) and SanText+ comparison
Performance analysis in various tasks (QA, summarization, code generation)
Results and Evaluation
ProSan's PHR and task performance
Usability impact analysis
Advantages over baseline methods
Discussion
Comparison with existing privacy solutions
Trade-offs between privacy and usability
Limitations and future improvements
Future Work
Exploration of advanced privacy techniques
Expanding ProSan's applicability to diverse chatbot scenarios
Conclusion
Summary of ProSan's contributions
Implications for privacy-conscious chatbot development
Recommendations for future research directions
Key findings
7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of privacy concerns related to online chatbots, specifically focusing on the inadvertent exposure of sensitive information in prompts to large language models (LLMs) . This problem is not entirely new, as previous works have attempted solutions based on local deployment, embedding perturbation, and homomorphic encryption, but these methods have limitations in terms of usability, computational costs, and system modifications . The paper introduces a novel framework called Prompt Privacy Sanitizer (ProSan) to produce anonymized prompts that remove contextual privacy while maintaining task usability and human readability, thereby offering a more effective solution to the privacy challenges associated with online prompt-based LLM applications .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to privacy protection in online prompt-based large language model (LLM) applications. It introduces the Prompt Privacy Sanitizer (ProSan), an end-to-end framework designed to produce anonymized prompts by removing contextual privacy while ensuring task usability and human readability . The hypothesis revolves around addressing privacy concerns associated with prompts inadvertently containing sensitive information exposed to LLMs, and the need for effective privacy protection mechanisms in online LLM services . The study focuses on achieving high usability and dynamic anonymity by adjusting protection targets and strength based on word importance and privacy leakage risks in prompts, catering to diverse computational resource conditions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" proposes several innovative ideas, methods, and models related to privacy and utility in text data processing . Here are some key points from the paper:

  1. Sanitizing Sentence Embeddings for Local Differential Privacy: The paper introduces a method for sanitizing sentence embeddings and labels to ensure local differential privacy, which is crucial for protecting sensitive information in text data .

  2. Sentence-Level Privacy for Document Embeddings: It discusses a technique for ensuring privacy at the sentence level in document embeddings, which is essential for maintaining confidentiality in text data processing .

  3. Privacy-and Utility-Preserving Textual Analysis: The paper presents a model that focuses on preserving privacy and utility in textual analysis through calibrated multivariate perturbations, offering a balance between data protection and data usability .

  4. Auto-Encoder Based Differentially Private Text Transformation: It introduces a method called Adept, which is based on auto-encoder for achieving differentially private text transformation, enhancing privacy in text data processing .

  5. Anonymization Models for Text Data: The paper explores anonymization models for text data, addressing the challenges and future directions in anonymizing text to prevent privacy disclosure by removing personally identifiable information (PII) .

  6. Prompt Privacy Protection: It discusses a lightweight framework called Hide and Seek (HAS) for prompt privacy protection, which is designed to enhance privacy in text data processing .

  7. Language Model Privacy: The paper delves into methods like natural language understanding with privacy-preserving BERT and differentially private prompt learning for large language models to ensure privacy in language processing tasks .

These innovative ideas, methods, and models proposed in the paper contribute significantly to the field of privacy preservation and utility enhancement in text data processing, offering valuable insights into safeguarding sensitive information while maintaining data usability and effectiveness. The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" introduces the Prompt Privacy Sanitizer (ProSan) framework, which offers several key characteristics and advantages compared to previous methods in the field of privacy protection in text data processing .

  1. Dynamic Anonymity and Usability Balance: ProSan dynamically adjusts its protection targets and strength based on the importance of words and the privacy leakage risk of prompts, ensuring a balance between usability and anonymity .

  2. Seamless Integration into Online LLM Service Pipeline: ProSan can be seamlessly integrated into the online Large Language Model (LLM) service pipeline, allowing for efficient and effective privacy protection in prompt-based LLM applications without excessive system modifications .

  3. Adaptability to Computational Resource Conditions: ProSan is capable of adapting to diverse computational resource conditions, ensuring privacy protection even for mobile devices with limited computing power, addressing the challenge of resource constraints in privacy preservation .

  4. Automated Prompt Anonymization Scheme: The framework includes an automated prompt anonymization scheme that generates massive original prompt-anonymized prompt pairs as training data, reducing the computational burden on users and enhancing adaptivity .

  5. Effective Removal of Private Information: ProSan effectively removes private information across various tasks such as question answering, text summarization, and code generation, with minimal reduction in task performance, showcasing its effectiveness in privacy protection .

  6. Enhanced Task Performance: Despite focusing on privacy protection, ProSan maintains task performance by effectively balancing the removal of private information with task usability, ensuring that the anonymized prompts remain usable and maintain task integrity .

  7. Superior Precision and Recall: ProSan outperforms other methods in distinguishing names with varying importance, ensuring superior precision, recall, and F1 scores for anonymization tasks related to name privacy attributes, highlighting its effectiveness in preserving usability while protecting privacy .

Overall, the characteristics and advantages of the ProSan framework, as detailed in the paper, demonstrate its effectiveness in achieving dynamic anonymity, seamless integration, adaptability to resource constraints, automated prompt anonymization, task performance maintenance, and superior precision and recall in privacy protection tasks compared to previous methods in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of balancing usability and privacy in prompts. Noteworthy researchers in this area include C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, D. Yang , J. Devlin, M.-W. Chang, K. Lee, K. Toutanova , B. Lester, R. Al-Rfou, N. Constant , A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi`ere, N. Goyal, E. Hambro, F. Azhar et al. , M. Du, X. Yue, S. S. Chow, H. Sun , C. Meehan, K. Mrini, K. Chaudhuri , O. Feyisetan, B. Balle, T. Drake, T. Diethe , S. Krishna, R. Gupta, C. Dupuy , Y. Chen, T. Li, H. Liu, Y. Yu , S. Utpala, S. Hooker, P.-Y. Chen , A. Zou, Z. Wang, J. Z. Kolter, M. Fredrikson , and many more.

The key to the solution mentioned in the paper is the "Prompt Privacy Sanitizer" (ProSan), which is an end-to-end prompt privacy protection framework. ProSan aims to produce anonymized prompts by removing contextual privacy while ensuring task usability and human readability. It can be seamlessly integrated into the online Large Language Model (LLM) service pipeline. ProSan adjusts its protection targets and strength based on word importance and privacy leakage risk in prompts, ensuring high usability and dynamic anonymity. Additionally, ProSan can adapt to diverse computational resource conditions, making it suitable for privacy protection even on mobile devices with limited computing power .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of the ProSan framework in protecting privacy in user prompts submitted to online large language models (LLMs) . The ProSan framework aims to produce anonymized prompts by dynamically balancing usability and anonymity, considering the importance of words and the privacy leakage risk of the prompts . The experiments demonstrated that ProSan effectively removes private information across various tasks, such as question answering, text summarization, and code generation, with minimal reduction in task performance .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the MedQA dataset, which includes multiple-choice questions related to medical information . The code used in the study is not explicitly mentioned as open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper introduces Prompt Privacy Sanitizer (ProSan), a framework designed to protect privacy in online chatbot applications by anonymizing prompts while maintaining usability and readability . The experiments conducted demonstrate that ProSan effectively removes private information across various tasks like question answering, text summarization, and code generation with minimal impact on task performance . The results show that ProSan adjusts its protection targets and strength based on the importance of words and the privacy leakage risk of prompts, ensuring high usability and dynamic anonymity .

Furthermore, the experiments reveal that ProSan's usability remains high, with only a minimal decrease in accuracy ranging from 0.4% to 4.6%, even for resource-limited versions of the framework . In contrast, other methods like HaS and SanText+ exhibit significant drops in accuracy, up to 39.6%, due to overly broad or rigid protection goals . This comparison highlights the effectiveness of ProSan in balancing usability and privacy protection, supporting the scientific hypotheses put forth in the paper .


What are the contributions of this paper?

The paper "The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts" introduces the Prompt Privacy Sanitizer (ProSan), which is an end-to-end framework designed to protect privacy in prompts while maintaining usability and human readability . The key contributions of this paper include:

  • Introducing ProSan, a framework that produces anonymized prompts by removing contextual privacy, ensuring task usability, and seamlessly integrating into online large language model (LLM) services .
  • Flexibly adjusting protection targets and strength based on word importance and privacy leakage risk in prompts, ensuring high usability and dynamic anonymity .
  • Demonstrating the effectiveness of ProSan in removing private information across various tasks like question answering, text summarization, and code generation with minimal impact on task performance .

What work can be continued in depth?

Further work that can be continued in depth includes:

  • Exploring the effectiveness of text privacy protection by developing objective metrics for evaluating the anonymization of prompts .
  • Investigating the impact of privacy leakage in prompts on achieving secure general artificial intelligence, especially in the field of natural language processing .
  • Conducting research on privacy attacks on language models, such as multi-step jailbreaking attacks, to enhance the security of online prompt-based LLM applications .
  • Developing frameworks and methodologies for protecting user privacy in remote conversational systems through privacy-preserving text sanitization techniques .
  • Exploring model compression and acceleration techniques for pretrained language models to enhance inference efficiency and reduce computational burden .
  • Continuing research on privacy requirements engineering, named entity recognition-based privacy approaches, and machine learning systems for redacting documents to address privacy concerns in text data .
Tables
2
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