As organizations increasingly integrate large language models (LLMs) like GPT-4 into their operations—ranging from customer service chatbots to data analysis tools—they face significant privacy challenges. Handling sensitive information and personally identifiable information (PII) requires meticulous attention to data protection regulations and ethical considerations. The risk of data breaches or misuse of PII not only undermines customer trust but also exposes organizations to legal repercussions.
Several strategies have emerged to address these privacy concerns:
• PII Detection and Redaction: Models such as PII-RANHA can identify and remove PII from datasets before processing, mitigating the risk of sensitive data exposure.
• Synthetic Data Generation: Utilizing generative methods to create synthetic datasets that retain the statistical properties of real data without containing actual PII, thus facilitating safe data sharing and model training (Source).
• AI-Driven Anonymization: Employing advanced AI techniques to anonymize data while preserving its utility for analysis (Source).
Organizations must decide whether to:
• Anonymize Data Before Sharing: Apply anonymization techniques and then send data to third-party model providers.
• Directly Use Third-Party Models: Send data as-is to external providers, relying on their compliance measures.
• Adopt On-Premise Solutions: Implement models within their own infrastructure to keep data entirely in-house.
Each option presents trade-offs in terms of privacy, cost, scalability, and performance. Navigating these choices is crucial for organizations aiming to leverage LLMs while maintaining compliance and trust.
The goal of this thesis is to conduct a structured literature review to explore and evaluate the various strategies available for privacy-compliant implementation of large language models in organizational settings. The student will:
• Analyze Privacy-Preserving Techniques: Investigate methods such as PII detection, synthetic data generation, and AI-driven anonymization.
• Compare Deployment Strategies: Assess the implications of using third-party providers versus on-premise models, considering factors like data security, compliance, and operational efficiency.
• Develop a Decision Framework: Create a structured approach to help organizations choose the most suitable privacy strategy and model selection based on their specific needs.
• Provide Recommendations: Offer best practices for balancing the benefits of LLMs with stringent privacy requirements.
• Interest in Privacy and AI: A keen interest in data privacy, data protection laws, and artificial intelligence applications.
• Research Skills: Ability to conduct comprehensive literature reviews and synthesize complex information.
• Analytical Thinking: Strong critical thinking skills to evaluate and compare different strategies.
• Independent Work Ethic: Self-motivated with a proactive approach to problem-solving.
• Communication Skills: Proficiency in scientific writing in English; ability to articulate findings clearly.
• Expert Supervision: Guidance from researchers at our chair.
• Supportive Environment: Regular meetings and feedback.
• Flexible Timeline: Opportunity to start immediately.
• Impactful Research: Chance to contribute to a critical area affecting modern organizations.
If you are interested in undertaking this meaningful and challenging project, please submit to marc.grau@unisg.ch:
• Your CV: Highlighting relevant experience and skills.
• Academic Transcripts: Providing a record of your academic performance.
• Motivation Letter: A brief statement (max. 200 words) expressing your interest in privacy considerations within large language model selection.