Privacy Scare of Using LLMs Finetuned for Domain-Specific Tasks in Business Scenarios
The HYPE
In the early 2010s, when there was a wave of social media platforms being introduced to the world, everyone, from a teen to an adult, was on a hunt to just sign up and send as many friend requests as possible. It was something “right” to do because it was a trend. In the field of technology whenever something new is created, the momentum it brings in the market is very strong and real. At this particular moment, something new is the Large Language models (LLMs), and from a startup to a large enterprise, everyone is looking to integrate LLMs into their existing workflow. We know how efficient the LLMs are for answering questions and for solving difficult problems. They can be equipped to run anything and everything. They are the goto friend who can answer any question. LLMs are quick, accurate and pretty elaborative when used correctly.
Generative AI is already being integrated in most of the existing business functions. In services, like chatbots, handling inquiries and solving problem at scale. In healthcare, AI models are used for diagnosing diseases from imaging data and predicting patient outcomes. In finance, AI helps in fraud detection and personalizes financial advice for clients. Marketing departments use AI to predict consumer behavior and personalize advertising. The common thread across these applications is the use of large language models (LLMs) that are often fine-tuned on domain-specific data to enhance their performance and relevance.
The PROBLEM
Despite the enthusiasm, the deployment of Gen-AI is not without challenges. Ethical concerns, such as bias in AI algorithms, transparency in AI decisions, and accountability, are significant issues. Regulatory compliance, especially with data protection laws like GDPR also poses hurdles. Following GDPR (EU Laws) compliance while developing in the field of AI is important for the future. It's something which will make sure that AI isn’t being used to manipulate humans. Businesses must ensure that their AI systems do not inadvertently violate privacy norms or ethical standards.
What bothers me today is how LLMs can be manipulated and tricked into retrieving the sensitive data which is being used to train them. Some companies are utilising LLMs to make their existing workflows faster and efficient and for that have to share propriety information during fine-tuning. This knowledge sharing, comes with a downside of attracting “Adversary Attacks”, a potential vulnerability which may degrade business quality.
One of the critical concerns with fine-tuning LLMs for specific domains involves the risks of “memorisation” and data leakage, which is leveraged critically by adversaries at large. LLMs, especially those trained in vast amounts of data, have the potential to memorize sensitive information. This risk is aggravated when models are fine-tuned on proprietary or confidential data. If these models inadvertently generate outputs containing these details, it could lead to significant privacy breaches and data leakage. For instance, an LLM trained in patient health records might memorize and subsequently may output some identifiable patient information under certain prompt conditions, breaching confidentiality agreements and violating privacy laws. Similarly, models trained on financial data could inadvertently expose the personal financial details of individuals if not adequately safeguarded.
Every day, n-number of attacks are being developed to extract training data from Large Language models. There are various types of attacks an adversary can utilize to extract training data, for, e.g. memorisation-focused attacks, known as Membership inference attacks, which can critically infer the membership status of a target data point, and there are many more. What's important is to understand how one can prevent or substantially reduce the leakage of training data to prevent any harm or loss incurred.
The SOLUTION
While one direct approach involves redacting sensitive information from the dataset but this often compromises data quality and can lead to poorer model performance. An alternative strategy involves the generation of synthetic data. This method allows for the preservation of the dataset’s overall integrity and utility while ensuring sensitive data remains protected. By maintaining the structure and diversity of the original data without eliminating potential data points, we can achieve a balance between privacy and model effectiveness.
Some popular synthetic data generators are as follows considering easy-to-advanced use case:
1. Faker Python library — A Python library that generates fake data such as names, addresses, and phone numbers. It’s very versatile and can be used for creating large datasets for testing and development.
2. yData.ai — This platforms offers robust capabilities for generating synthetic data through its ydata-synthetic package. This package is equipped to handle both tabular and time-series data and supports a variety of Generative Model architectures.
The integration of Generative AI into business practices offers immense potential but also requires careful consideration of privacy and security implications. Enterprises and startups must implement robust data handling and model training practices to mitigate risks like data leakage and memorization.
