## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Technologies for Assuring Data Integrity in Healthcare AI
#### 4.2.1 Homomorphic Encryption (HE): Enhancing Data Confidentiality  
Homomorphic Encryption (HE) is transformative in securing sensitive healthcare data. By enabling computations on encrypted data, HE ensures data confidentiality even during processing. This feature is critical when handling sensitive information such as genomic data, which forms the cornerstone of precision medicine and other advanced medical research. With its complex and personal nature, genomic data demand stringent privacy measures. HE meets this demand by allowing data owners to encrypt their data before sending it for computation, thus ensuring that sensitive information remains confidential throughout the process. The computational tasks are performed on this encrypted data without decryption, preserving data attributes and ensuring that third parties can process it without accessing its original, unencrypted form. This aspect of HE is particularly beneficial in scenarios where data need to be shared among multiple researchers or institutions. It provides a secure way to collaborate and gain insights from sensitive data without compromising privacy, thus fostering a more collaborative and productive healthcare research environment [9]. Building upon this understanding, the analysis by [20] delves into the multifaceted significance of HE in healthcare, particularly in the context of cloud computing and cloud storage. Traditional encryption methods, which decrypt data before processing, risk exposing sensitive medical information. HE circumvents this risk by allowing computations on encrypted data, thereby maintaining the confidentiality of sensitive patient information, such as genomic data, throughout the processing phase. This is especially pertinent for genomic data, which are integral to precision medicine and advanced medical research. By encrypting this data prior to computation, HE ensures privacy preservation, even when third parties are involved in handling the data. The increasing adoption of cloud computing in healthcare amplifies the need to protect patient-sensitive data. HE addresses this need by enabling data processing in an encrypted form, ensuring that service providers only have access to encrypted data, thereby maintaining privacy and confidentiality. This is a critical consideration in healthcare environments where data are frequently shared across various platforms and institutions, and robust encryption methods are required to prevent unauthorized access. Moreover, HE is pivotal in collaborative healthcare research and data sharing. It allows for the secure sharing of sensitive data among multiple researchers or institutions without compromising privacy. This capability is essential for collaborative healthcare research, where gaining insights from sensitive data is necessary but must be carried out without revealing the actual data. The ability to perform computations on encrypted data enables a secure and productive exchange of information, which advances medical research while adhering to strict privacy standards. The evolution of HE has introduced various schemes, each with its own capabilities and limitations. Starting with Partial Homomorphic Encryption (PHE), which supports limited operations, the field has progressed to Somewhat Homomorphic Encryption (SWHE) schemes that allow for an arbitrary number of operations but are constrained by noise generation. The advent of Fully Homomorphic Encryption (FHE) marked a significant breakthrough by supporting an unlimited number of operations on encrypted data. Despite this advancement, FHE still grapples with noise management challenges, necessitating bootstrapping and squashing techniques to maintain the integrity of the encryption. The technological underpinnings of advanced HE schemes are often based on the lattice-based approach, including Learning with Errors (LWE). The difficulty of solving lattice problems, which LWE is predicated upon, provides a strong foundation for cryptography, particularly in healthcare scenarios where data security is paramount.