Homomorphic Encryption: The 'Holy Grail' for Big Data Analytics and Legal Compliance in the Pharmaceutical and Healthcare Sector (original) (raw)
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SSRN Electronic Journal, 2019
The pharmaceutical and healthcare sector is a prime target for cybercriminals around the world. These cyber-attacks represent significant challenges in the context of data protection and data security. The General Data Protection Regulation (GDPR) imposes strict rules regarding the processing and analysis of personal data. In conventional approaches, data analysts request data from various sources. Then, they anonymise or pseudonymise the data using various tools and techniques. These methods often use powerful algorithms to ensure a high level of security. However, these methods tend to either reduce the quality of data for further analysis or they expose the data while decrypting it for analysis. Homomorphic Encryption (HE) has recently been touted as the 'Holy Grail' of cryptography since it allows the analysis of big data sets without ever needing to decrypt and thus compromising the confidentiality of the data. This provides a whole new layer of protection and at the same time allows the processing of data for secondary use and scientific research. While HE is not a new technology, it is still in the early stages of development. In this piece, we will introduce a new automated tool for searching and analysing encrypted data using HE techniques, which is being developed within the scope of the EnergyShield project. 1
Medical Data Analytics in the Cloud Using Homomorphic Encryption
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A systematic review of homomorphic encryption and its contributions in healthcare industry
Complex & Intelligent Systems
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 proces...
The Effectiveness of Homomorphic Encryption in Protecting Data Privacy.
International Journal of Research Publication and Reviews, Vol 5, no 11, pp 3235-3256 , 2024
As the use of digital services grows, protecting the privacy and integrity of sensitive data, especially in fields like healthcare, finance, and secure surveying, has become a critical concern. Homomorphic encryption (HE) offers a solution by allowing computations to be performed on encrypted data without revealing the original information. This paper examines the principles of homomorphic encryption and its applications in privacy-preserving tasks, focusing on its use in cloud computing, healthcare, and cybersecurity. Various types of HE schemes, including Fully Homomorphic Encryption (FHE), Partially Homomorphic Encryption (PHE), and Somewhat Homomorphic Encryption (SHE), are reviewed to assess their performance, efficiency, and real-world use. The paper also discusses the challenges of implementing HE, such as computational overhead and key management. It suggests directions for future research to improve the scalability and usability of HE in real-time applications. Addressing these challenges will make homomorphic encryption an essential tool for secure, privacy-preserving data processing and sharing in modern digital systems
The Next Frontier of Security: Homomorphic Encryption in Action
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024
Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.
Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms
Entropy
Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We t...
A Survey on Implementation of Homomorphic Encryption Scheme in Cloud based Medical Analytical System
The privacy of sensitive personal information is more and more important topic as a result of the increased availability of cloud services. These privacy issues arise due to the legitimate concern of a) having a security breach on these cloud servers or b) the leakage of this sensitive information due to an honest but curious individual at the cloud service provider. Standard encryption schemes try to address the first concern by devising encryption schemes that are harder to break, yet they don’t solve the possible misuse of this sensitive data by the cloud service providers. Homomorphic encryption presents a tool that can solve both types of privacy concerns. The clients are given the possibility of encrypting their sensitive information before sending it to the cloud. The cloud will then compute over their encrypted data without the need for the decryption key. By using homomorphic encryption, servers guarantee to the clients that their valuable information to have no problems after being in a difficult situation..
2014
This paper studies the issues of Big Data Analytics (BDAs) or the Internet of Things (IOT) and its implementation in the Cloud Computing Environment with a full thrust consideration to the Fully Homomorphic Encryption Scheme (FHE). The FHE is a computational algorithm that allows the computations on encrypted data; and yet, when the outputs of the computations of the encrypted data are decrypted, they still conform to the operations of the original plaintext. The Bootstrapping of the Somewhat Homomorphic Encryption Scheme (SWHE) of the Craig Gentry's PhD the- sis was studied; an algorithm that continuously reduces the inherently noisy ciphertext parameters of the Ideal Circuit at each computational iteration stage, and thus, gives more room for the less noisy ciphertext computation in the cloud based the application of addition and multiplication operations. The FHE implementation on Big Data Ana- lytcs Security on the cloud computing was discovered to have boosted confidentiali...
2016
The advancement in technology, industry, e-commerce and research. A large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as Big data. For analyze and handling such big data various tools are available .The cloud computing is resolved for the problems arises in big data storage. Data security is major issues in the cloud can be enhance by fully homomorphic encryption technique. As the cloud, data storage can be manage by clustering for security and privacy of data. In this paper, we have defined of fully homomorphic encryption technique and digital signature is applied to our system and according to that, it shown the output which provide the security to our system.