Protecting Patient Data in the Age of AI

Author: Denis Avetisyan

As large language models become increasingly integrated into healthcare, understanding and mitigating the unique privacy risks across their entire lifecycle is paramount.

Large language models are increasingly applied across diverse healthcare functions, encompassing tasks such as medical diagnosis, personalized treatment planning, and streamlined administrative processes.

This review systematically analyzes the threat model for healthcare LLMs and proposes a layered, lifecycle-aware approach to privacy-preserving techniques like federated learning and differential privacy.

Despite the transformative potential of Large Language Models (LLMs) in healthcare, their deployment introduces substantial privacy and security risks due to the sensitive nature of clinical data. This systematization of knowledge, ‘SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations’, comprehensively analyzes these threats across the entire LLM lifecycle-from data preprocessing and fine-tuning to inference-identifying vulnerabilities that accumulate across operational tiers. Our analysis reveals that while existing privacy-preserving techniques offer some mitigation, persistent limitations necessitate layered, phase-aware defenses. Can a holistic, lifecycle-focused approach to privacy truly unlock the benefits of LLMs in healthcare while maintaining patient trust and regulatory compliance?

Navigating the Privacy Landscape of Large Language Models

The escalating integration of Large Language Models into healthcare presents substantial privacy challenges, as these models are frequently exposed to highly sensitive clinical data. This data, encompassing patient records, diagnoses, and treatment plans, becomes vulnerable throughout the LLM’s operational lifespan. Unlike traditional data processing systems, LLMs ‘learn’ from the data they are fed, potentially memorizing and inadvertently revealing private information during text generation or through model inversion attacks. The very nature of LLMs – their capacity to identify patterns and extrapolate from data – increases the risk of re-identification, even when data has been superficially de-identified. Consequently, the expanding use of LLMs in clinical settings necessitates a proactive re-evaluation of existing privacy safeguards and the development of novel techniques specifically tailored to mitigate these emerging vulnerabilities.

Conventional privacy-preserving techniques, such as data masking and differential privacy, frequently fall short when applied to Large Language Models due to the models’ inherent complexities and the multifaceted nature of their lifecycle. These methods often struggle to account for the ways LLMs memorize training data, potentially revealing sensitive information through generated text, or how data transforms across stages like fine-tuning and prompt engineering. Moreover, traditional approaches typically focus on data at rest or in transit, neglecting the privacy risks introduced during model inference-when the model is actively processing requests. The unique capacity of LLMs to generalize and recombine information means that even anonymized data can be re-identified or that unintended biases are amplified, necessitating a shift towards LLM-specific privacy solutions that address risks across the entire workflow – from initial data collection to ongoing model deployment and monitoring.

The intricate journey of data within Large Language Models presents numerous avenues for privacy breaches. Data isn’t simply inputted and outputs generated; instead, it undergoes extensive preprocessing-including cleaning, tokenization, and potential augmentation-which can inadvertently expose sensitive information. During model training, data is repeatedly processed and refined, increasing the risk of memorization and subsequent leakage through model weights. Even the inference stage-where the model responds to prompts-isn’t immune, as adversarial attacks or carefully crafted prompts can sometimes elicit private data. This multi-stage workflow, coupled with the inherent complexity of LLM architectures, dramatically expands the attack surface compared to traditional data processing systems, necessitating robust, lifecycle-aware privacy safeguards.

Responsible deployment of Large Language Models demands a meticulous assessment of privacy vulnerabilities throughout their entire lifecycle, a need addressed by a recent systematization of potential risks. This work details how privacy concerns aren’t isolated to a single stage – such as model training – but rather manifest and evolve across data preprocessing, model development, deployment, and ongoing monitoring. By comprehensively mapping these risks, researchers emphasize that effective mitigation strategies must be equally granular and phase-specific. Ignoring vulnerabilities at any point – from the initial data handling to the final inference stage – can expose sensitive information and undermine trust in these increasingly powerful technologies. This detailed lifecycle analysis provides a crucial framework for developers and organizations aiming to leverage the benefits of LLMs while upholding robust privacy standards.

Building a Secure Foundation: Data Preprocessing for Privacy

Data preprocessing represents a primary attack vector for privacy breaches because it’s the initial stage where raw, potentially identifying information is handled. Consequently, employing techniques like anonymization and synthetic data generation at this stage is crucial for mitigating risk. Anonymization involves removing or obscuring personally identifiable information (PII), while synthetic data generation creates entirely new datasets that statistically resemble the original data without containing actual individual records. These methods proactively reduce the exposure of sensitive data before it enters subsequent processing pipelines, establishing a foundational layer of privacy protection and minimizing the potential for re-identification attacks.

Tokenization, the process of replacing sensitive data with non-sensitive substitutes, is a common data security practice; however, inadequate management introduces significant privacy risks. If the tokenization process lacks robust key management, the mapping between tokens and original data becomes a single point of failure. Furthermore, the storage of tokenization keys must adhere to strict security protocols to prevent unauthorized access and re-identification of data. Insufficiently randomized or predictable token generation algorithms can also be vulnerable to attacks that reverse the tokenization process. Logging or auditing of tokenized data, without proper redaction, can inadvertently expose underlying sensitive information. Finally, failing to account for the lifespan of tokens and implementing appropriate rotation policies creates extended windows of vulnerability.

Differential Privacy (DP) introduces a mathematically rigorous framework for quantifying privacy loss during data preprocessing. Unlike methods like anonymization which can be vulnerable to re-identification, DP guarantees that the addition or removal of a single individual’s data has a limited effect on the outcome of any analysis. This is achieved by adding carefully calibrated noise to the data or query results, controlled by a parameter called epsilon (ε). A lower ε value indicates stronger privacy, but may reduce data utility. DP mechanisms, such as the Laplace or Gaussian mechanism, provide provable bounds on the privacy loss, allowing data owners to confidently assess and communicate the level of privacy afforded to individuals within the dataset before further processing or analysis.

A robust privacy foundation is achieved by combining data preprocessing techniques; specifically, initial anonymization or synthetic data generation establishes a primary defense, followed by careful tokenization management to mitigate associated risks. Integrating Differential Privacy during this preprocessing phase adds a mathematically quantifiable privacy guarantee, limiting the potential for re-identification. This layered approach, utilizing multiple complementary methods, significantly reduces the attack surface and creates a strong baseline for subsequent privacy-preserving operations, ensuring consistent protection throughout the entire data lifecycle.

Decentralized Learning: Federated Learning and Secure Model Adaptation

Federated Learning (FL) is a distributed machine learning approach that allows for model training on a decentralized network of devices or servers holding local data samples, without exchanging those data samples. Instead of aggregating data into a centralized repository, FL algorithms train models locally on each device, and then only share model updates – such as gradients or model weights – with a central server. This aggregation of updates enables the construction of a global model while keeping the training data localized, thereby reducing the risk of data breaches and enhancing data privacy compared to traditional centralized learning approaches. The primary benefit is the mitigation of centralized privacy risks associated with storing and processing sensitive data in a single location, addressing growing data governance concerns and regulatory requirements.

Secure aggregation protocols are a critical component of federated learning, designed to protect the privacy of individual client updates during model training. These protocols enable a central server to compute an aggregate of model updates from multiple clients without directly accessing the individual updates themselves. This is typically achieved through cryptographic techniques, such as additive secret sharing or homomorphic encryption, where each client encrypts or masks its update before sending it to the server. The server then operates on these encrypted/masked updates, producing an aggregated result which can be decrypted or unmasked to obtain the combined model update. This ensures that even if the server is compromised, the individual contributions of each client remain confidential, preserving data privacy throughout the federated fine-tuning process.

Differential Privacy (DP) enhances privacy in federated learning by adding carefully calibrated noise to the model updates shared by participating clients. This noise obscures the contribution of any single client’s data, preventing the reconstruction of individual records while still allowing the model to learn effectively from the aggregate updates. DP is typically quantified by ε and δ, parameters that define the privacy loss; lower values indicate stronger privacy guarantees. Mechanisms like DP-SGD, which adds noise to the gradients during stochastic gradient descent, and private aggregation of model updates are commonly employed to achieve DP in federated settings. The magnitude of the added noise is directly related to the sensitivity of the function being privatized and the desired privacy level.

Data poisoning attacks represent a significant threat during federated training, where malicious participants intentionally submit corrupted data to influence the global model. These attacks can compromise both model integrity, leading to inaccurate predictions, and user privacy, potentially enabling inference of sensitive information from the poisoned model. Common poisoning strategies include label flipping, where incorrect labels are assigned to data, and backdoor attacks, which embed hidden triggers into the model. Mitigation techniques involve robust aggregation rules, such as median or trimmed mean, to reduce the impact of outliers, and anomaly detection algorithms to identify and filter potentially malicious contributions. Furthermore, techniques like differential privacy can offer some resilience against poisoning by adding noise to individual updates, though this may come at the cost of model accuracy.

Shielding Inference: Advanced Privacy-Preserving Techniques

The ability to perform computations directly on encrypted data represents a paradigm shift in privacy-preserving machine learning, and techniques like Homomorphic Encryption are central to this advancement. Traditionally, data must be decrypted before processing, exposing it to potential vulnerabilities; however, Homomorphic Encryption enables algorithms to operate on ciphertext – data in its encrypted form – without prior decryption. This means models can generate inferences on sensitive data while it remains protected, significantly reducing the risk of data breaches and unauthorized access. Different schemes exist, each with trade-offs between computational cost and the types of operations supported, but the core principle allows for secure inference without compromising data confidentiality. This capability is particularly crucial in fields like healthcare and finance, where data privacy is paramount and the need for predictive modeling remains strong, opening possibilities for collaborative analysis without directly sharing raw data.

Local Differential Privacy (LDP) represents a powerful approach to safeguarding individual data during machine learning inference. Unlike traditional privacy methods that focus on anonymizing entire datasets, LDP introduces calibrated noise directly to each individual data point before it is used for training or prediction. This ensures that the model learns from perturbed data, effectively masking the contribution of any single individual and providing mathematically provable privacy guarantees. The strength of these guarantees is controlled by a privacy parameter, often denoted as ε (epsilon), which quantifies the trade-off between privacy and data utility – a smaller ε indicates stronger privacy but potentially reduced model accuracy. By applying LDP at the input level, systems can offer strong individual privacy without requiring access to or trust in centralized data controllers, making it particularly valuable in decentralized or federated learning scenarios where data resides on user devices.

Trusted Execution Environments (TEEs) represent a significant advancement in safeguarding machine learning inference against increasingly sophisticated malicious attacks. These secure enclaves, often built into modern processors, create isolated, hardware-protected regions where sensitive computations can occur. During inference, a model – or portions of it – can be loaded into the TEE, shielding it from software-based attacks that might attempt to steal the model’s parameters or manipulate the inference process. This isolation extends to the data being processed, ensuring confidentiality even if the operating system or other software components are compromised. By verifying the integrity of the code and data within the TEE, and attesting to a remote party that the inference is occurring in a trusted environment, these systems offer a robust defense against model theft, data breaches, and adversarial manipulations, ultimately fostering greater confidence in deploying machine learning applications in untrusted environments.

Model extraction attacks pose a significant threat during the inference stage of machine learning, as adversaries attempt to reconstruct the underlying model by repeatedly querying it. This isn’t about stealing training data; instead, attackers build a functionally equivalent model, potentially revealing intellectual property or sensitive algorithmic details. Successful extraction can occur even without knowing the model’s architecture or parameters, relying solely on observed input-output behavior. Defenses include limiting query access – capping the number of requests or introducing noise to responses – and employing adversarial training techniques that make the model more robust against reconstruction attempts. Furthermore, techniques like watermarking embed detectable signals within the model’s responses, allowing owners to prove ownership and detect unauthorized copies. Preventing model extraction is therefore crucial not only for protecting competitive advantage but also for safeguarding privacy, as a reconstructed model can be further analyzed to infer characteristics of the original training data.

A Holistic Vision: The Future of LLM Privacy

Large language models present unique privacy challenges due to the potential for sensitive information to leak across distinct phases of their lifecycle – from initial data collection and model training, through deployment and ongoing refinement. This paper details how privacy risks aren’t isolated to a single stage; rather, vulnerabilities can propagate, meaning a seemingly minor lapse in security during data sourcing can have significant repercussions later in the model’s operational use. Consequently, a fragmented approach to privacy protection is insufficient. The research advocates for a unified framework – encompassing data minimization, differential privacy, secure multi-party computation, and federated learning – applied consistently throughout the entire LLM lifecycle. This holistic strategy, the authors contend, is essential not only for identifying and mitigating existing risks but also for proactively addressing unforeseen vulnerabilities as LLMs become increasingly complex and integrated into everyday applications.

A robust strategy for safeguarding data within large language models necessitates a layered, defense-in-depth approach, rather than reliance on any single technique. Combining methods like differential privacy – which adds carefully calibrated noise to data – with federated learning, where model training occurs on decentralized datasets, creates a more resilient system. Further bolstering security, techniques such as homomorphic encryption allow computations on encrypted data, preventing exposure during processing, while secure multi-party computation enables collaborative analysis without revealing individual datasets. This multi-faceted strategy acknowledges that each privacy-enhancing technology has limitations; by integrating several, the overall system becomes significantly more resistant to attacks and data breaches, ensuring a more trustworthy and secure user experience.

The rapidly evolving landscape of large language models (LLMs) necessitates continuous innovation in privacy-preserving technologies. Current techniques, while valuable, are often reactive to newly discovered vulnerabilities and attack vectors. Therefore, dedicated research into areas like differential privacy, federated learning, and homomorphic encryption – alongside the development of entirely new approaches – is paramount. This ongoing effort isn’t simply about patching existing flaws; it’s about proactively building LLMs with privacy baked into their core architecture. Such research explores methods to minimize data exposure during training, inference, and deployment, ultimately aiming to create systems resilient to future, currently unforeseen, threats and fostering greater user trust in increasingly powerful AI applications.

Building user confidence in large language models necessitates a fundamental shift towards proactive privacy engineering and responsible AI development. This entails integrating privacy considerations at every stage of the LLM lifecycle – from data collection and model training to deployment and ongoing monitoring – rather than treating them as afterthoughts. Such practices involve techniques like differential privacy, federated learning, and secure multi-party computation, coupled with rigorous auditing and transparency regarding data usage. Prioritizing fairness, accountability, and explainability alongside privacy isn’t merely an ethical imperative; it’s a critical factor in fostering public acceptance and unlocking the full potential of these powerful technologies, ensuring long-term viability and trust in an increasingly AI-driven world.

The systematization presented underscores a critical point: each stage of an LLM’s lifecycle introduces new dependencies, and consequently, potential vulnerabilities. This echoes Henri Poincaré’s observation: “It is through science that we arrive at truth, but it is through art that we express it.” The ‘art’ here lies in the design of layered defenses. Just as a complex system’s behavior is dictated by its structure, the privacy of a healthcare LLM is determined by the interplay of its components – from data preprocessing to inference. Every new dependency, every added layer of complexity, is a hidden cost, demanding careful consideration to maintain the integrity and trustworthiness of the system as a whole. The paper’s focus on lifecycle-aware privacy embodies this principle, advocating for a holistic approach to security.

The Road Ahead

The systematization of privacy risks across the healthcare LLM lifecycle reveals a predictable truth: complexity begets vulnerability. Current defenses, while necessary, often resemble patching leaks in a ship already taking on water. The field fixates on individual attacks – differential privacy here, federated learning there – without adequately addressing the accumulating fragility inherent in multi-stage systems. A truly robust approach demands a shift in perspective; privacy must be engineered as a foundational property, not a bolted-on afterthought.

Future work should prioritize the development of formal methods for verifying privacy guarantees across the entire LLM lifecycle. The current reliance on empirical evaluation is insufficient; guarantees, however limited, are preferable to optimistic assumptions. Furthermore, investigation into the interplay between different privacy-enhancing techniques is crucial. Layered defenses are only effective if their combined effect is understood, and their potential for interference is mitigated.

Ultimately, the pursuit of privacy in healthcare LLMs is not merely a technical challenge, but a philosophical one. If a privacy solution feels clever, it is probably fragile. Simplicity, clarity, and a deep understanding of systemic behavior will be the hallmarks of lasting success. The goal should not be to eliminate risk entirely-an impossible task-but to build systems that degrade gracefully, and reveal their failures predictably.

Original article: https://arxiv.org/pdf/2601.10004.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/