Privacy in Large Language Models: Attacks, Defenses and Future Directions (2310.10383v2)

Abstract: The advancement of LLMs has significantly enhanced the ability to effectively tackle various downstream NLP tasks and unify these tasks into generative pipelines. On the one hand, powerful LLMs, trained on massive textual data, have brought unparalleled accessibility and usability for both models and users. On the other hand, unrestricted access to these models can also introduce potential malicious and unintentional privacy risks. Despite ongoing efforts to address the safety and privacy concerns associated with LLMs, the problem remains unresolved. In this paper, we provide a comprehensive analysis of the current privacy attacks targeting LLMs and categorize them according to the adversary's assumed capabilities to shed light on the potential vulnerabilities present in LLMs. Then, we present a detailed overview of prominent defense strategies that have been developed to counter these privacy attacks. Beyond existing works, we identify upcoming privacy concerns as LLMs evolve. Lastly, we point out several potential avenues for future exploration.

Privacy in LLMs: Attacks, Defenses and Future Directions

The paper "Privacy in LLMs: Attacks, Defenses and Future Directions" offers an extensive analysis of privacy issues prevalent in the use of LLMs. It highlights the dual role LLMs play in enhancing accessibility and usability in NLP applications while simultaneously introducing potential privacy risks. The authors categorize privacy attacks based on the adversary's capabilities, discuss various defense strategies, and propose future research avenues in privacy preservation for LLMs.

Privacy Risks in LLMs

LLMs, such as those developed by OpenAI and Google, trained on massive datasets, have demonstrated significant advancements in unifying diverse NLP tasks into generative pipelines. Despite these successes, the paper argues that unrestricted access to LLMs poses privacy challenges, particularly relating to the exposure of personally identifiable information (PII) without user consent. This exposure creates potential conflicts with privacy regulations like GDPR and CCPA.

The authors identify key privacy threats in LLMs:

1. Training Data Privacy: The memorization tendencies of LLMs can reveal sensitive data during inference if the training data contains personal information.
2. Inference Data Privacy: User queries and inputs stored during inference may consist of private conversations and sensitive data.
3. Re-identification Risk: Even anonymized data is prone to re-identification by correlating information from different interactions with LLMs.

Category of Privacy Attacks

The paper meticulously categorizes several privacy attacks and assesses their effectiveness:

• Backdoor Attacks: These attacks involve the intentional insertion of triggers in datasets or models that produce intended outputs upon activation. The authors categorize these attacks into poisoned datasets, pre-trained models, and fine-tuned models. These vulnerabilities pose significant threats as poisoned models may reveal sensitive information or alter results maliciously.
• Prompt Injection Attacks: These attacks exploit LLMs' instruction-following abilities by manipulating prompts to yield undesired outputs. The paper highlights the risks associated with prompt injection in applications integrated with LLMs.
• Data Extraction Attacks: LLMs can leak training data, allowing attackers to recover memorized data. Empirical studies and benchmarks further quantify such data leakage.
• Membership Inference Attacks: Attackers aim to discern if specific inputs were part of the model's training data using the model's response patterns.
• Information-based Attacks: With additional access to embeddings or gradients, attackers may recover sensitive information, conduct attribute inference, or reverse-engineer data.

Defense Strategies

The paper provides a comprehensive overview of defense mechanisms designed to address privacy attacks:

• Differential Privacy (DP): DP-based defenses add random noise during model training to preserve privacy. Despite offering theoretical privacy guarantees, DP often compromises model utility, calling for a nuanced trade-off between privacy and performance.
• Secure Multi-Party Computation (SMPC): This cryptographic approach allows multiple parties to jointly compute model updates without revealing private data, optimizing model inference efficiency while retaining privacy.
• Federated Learning: By enabling collaborative model training without data sharing, federated learning provides an alternative that minimizes privacy risks.

Future Directions

The paper concludes with insights into future research directions, emphasizing the need to address limitations of existing privacy attacks and defenses. Potential avenues include:

1. Exploration of Prompt Injection: Developing robust defenses against prompt injection attacks, tailored to diverse applications of LLMs.
2. Advancements in SMPC: Integrating strengths of MSO and SPO to enhance the efficiency and versatility of privacy-preserving algorithms.
3. Human-centric Privacy Studies: Aligning privacy judgments with human perception, recognizing diverse privacy preferences across cultural, social, and individual dimensions.
4. Comprehensive Privacy Evaluation: Establishing empirical methods and metrics for evaluating privacy risks beyond simplistic formulations.
5. Contextual Privacy Judgment: Developing frameworks for nuanced privacy assessments within complex contexts such as multi-turn dialogues.

Overall, the paper serves as a valuable resource for understanding the multifaceted privacy concerns associated with LLMs. It underscores the necessity for continued research to navigate the evolving challenges in safeguarding user data in NLP technologies.