A Systematic Review of Homomorphic Encryption and Its Contributions in Healthcare Industry
Open accessAbstract
Cloud computing and cloud storage have contributed to a big shift in data processing and its use. Availability and accessibility of resources with the reduction of substantial work is one of the main reasons for the cloud revolution. With this cloud computing revolution, outsourcing applications are in great demand. The client uses the service by uploading their data to the cloud and finally gets the result by processing it. It benefits users greatly, but it also exposes sensitive data to third-party service providers. In the healthcare industry, patient health records are digital records of a patient's medical history kept by hospitals or health care providers. Patient health records are stored in data centers for storage and processing. Before doing computations on data, traditional encryption techniques decrypt the data in their original form. As a result, sensitive medical information is lost. Homomorphic encryption can protect sensitive information by allowing data to be processed in an encrypted form such that only encrypted data is accessible to service providers. In this paper, an attempt is made to present a systematic review of homomorphic cryptosystems with its categorization and evolution over time. In addition, this paper also includes a review of homomorphic cryptosystem contributions in healthcare.
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Homomorphic Encryption enables computations on encrypted data without the need to decrypt it first.
“The ability of HE to enable computations on encrypted data without the need to decrypt it first offers a powerful tool for maintaining privacy while still allowing for the valuable analysis and utilization of healthcare data by Artificial Intelligence (AI) systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
Homomorphic Encryption can address the twin challenges of privacy and data integrity in healthcare AI.
“The study by [20] delves into the intricacies of HE and its application in healthcare AI, providing a detailed exploration of how HE can address the twin challenges of privacy and data integrity.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
Traditional encryption methods decrypt data before processing, risking exposure of sensitive medical information.
“Traditional encryption methods, which decrypt data before processing, risk exposing sensitive medical information.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
HE circumvents this risk by allowing computations on encrypted data, maintaining confidentiality of sensitive patient information such as genomic data throughout processing.
“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.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
HE can assist healthcare organizations in meeting HIPAA regulatory requirements by providing a secure method for handling and analyzing patient data.
“HE can assist healthcare organizations in meeting these regulatory requirements by providing a secure method for handling and analyzing patient data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
LWE-based schemes have potential resistance to quantum computing attacks, making them viable for post-quantum cryptography.
“The robustness of LWE-based schemes is further underscored by their potential resistance to quantum computing attacks, making them a viable option for post-quantum cryptography and ensuring the long-term security of healthcare data [20].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
HE faces computational efficiency and implementation complexity challenges.
“Despite its promising potential, HE faces computational efficiency and implementation complexity challenges.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
FHE supports an unlimited number of operations on encrypted data.
“The advent of Fully Homomorphic Encryption (FHE) marked a significant breakthrough by supporting an unlimited number of operations on encrypted data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
FHE still grapples with noise management challenges, necessitating bootstrapping and squashing techniques.
“Despite this advancement, FHE still grapples with noise management challenges, necessitating bootstrapping and squashing techniques to maintain the integrity of the encryption.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
HE is pivotal in collaborative healthcare research and data sharing, allowing secure sharing of sensitive data among multiple researchers or institutions without compromising privacy.
“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.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
Advanced HE schemes are often based on the lattice-based approach including LWE.
“The technological underpinnings of advanced HE schemes are often based on the lattice-based approach, including Learning with Errors (LWE).”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [20]
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References 75 · Cited by 269
Research works printed but not linked: 15
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- Authors
- Munjal, K., & Bhatia, R.
- Affiliations
- Chandigarh University (IN)
- Punjabi University (IN)
- Corresponding author
- Munjal, Kundan
- Venue
- Complex & Intelligent Systems
- Published
- 2022-05-03
- Volume
- 9
- Issue
- 4
- Pages
- 3759-3786
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- PMC
- PMC9062639
- OpenAlex
- W4225377307
- Cited by
- 269
- References
- 75
- Citations by year
- 2026: 51 · 2025: 100 · 2024: 81 · 2023: 29 · 2022: 8
- FWCI
- 3.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- homomorphic encryption, healthcare data privacy, patient health records, cloud security, cloud storage
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