International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Energy efficient privacy preserving in Medical Cyber Physical Systems Tanay Chillal1, Ajinkya Mhatre2, Dr. S.B Deshmukh3 1Student, Pune Institute of Computer Technology, Pune
2Student, Pune Institute of Computer Technology, Pune 3Professor, Pune Institute of Computer Technology, Pune
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Abstract - In the upcoming years, the Medical Cyber
evaluations based on patient health records without divulging specific details. Similarly, a patient may desire a centralized web service for storing and managing medical records while harboring concerns about the confidentiality of private health data. In such cases, obtaining health status information, such as disease predictions, becomes crucial [2]. Homomorphic encryption provides a solution by enabling computations on encrypted data without the need for decryption. This allows scenarios where a cloud prediction service can assess the likelihood of contracting a disease without exposing the actual medical record. The computation occurs without decrypting the data, and the result is delivered in encrypted form. The patient, upon receiving the encrypted prediction on a local device, decrypts it to access the prediction. This approach ensures that the cloud service only interacts with encrypted data, preserving the privacy of the underlying information [3].
Physical Systems (MPCS) will have a huge impact in transforming the healthcare industry. The collected data can be sent by these MCPS to a public or private cloud for processing and archiving. Healthcare practitioners may receive decision support from machine learning algorithms processing this data on the cloud. In addition to it, algorithms running on cloud can provide predictive results based on the medical reports, patient data and background. These systems possess a problem of privacy breach as data to be computed is exposed to cloud vendors. Fully Homomorphic Encryption (FHE) solves this problem as it allows computations and operations to be performed on encrypted data, without decrypting the data, and without even needing the decryption key. In this paper, we discuss applications of homomorphic encryption in order to ensure privacy of sensitive medical data and survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Privacy is preserved as cloud handles only the encrypted data and decryption is performed at the side of patient and authorized health professionals.
Traditional encryption methods are highly efficient but lack the capability to perform computations on encrypted data. In contrast, homomorphic encryption (HE) schemes enable the execution of meaningful operations on encrypted data without revealing the actual information. By employing HE, both storage and computation tasks can be delegated to public cloud operators, addressing concerns related to data privacy in medical cloud computing. A Fully Homomorphic Encryption (FHE) scheme is achieved when it can evaluate arbitrary functions. To perform such evaluations on ciphertexts, FHE schemes must conduct both homomorphic addition and homomorphic multiplication, equivalent to the addition and multiplication of plaintext messages, respectively [4][5].
Key Words: Medical cyber physical systems (MCPS), medical data privacy, homomorphic encryption, attribute-based encryption, disease risk prediction, Internet of Things (IoT).
1.INTRODUCTION In the healthcare domain, maintaining the confidentiality of sensitive patient records is paramount. Ensuring the privacy of such information is achievable through encryption, where the data owner encrypts the information before uploading it to a cloud service. This approach ensures that only the authorized data owner can access the data by decrypting it using their private decryption key. However, this encryption introduces challenges when it comes to outsourcing computations on externally stored information, particularly if the datacenter lacks access to the decryption key. Standard encryption schemes require the decryption key for performing computations on the data, making tasks like searching an encrypted database or conducting statistical analyses computationally intensive [1]. Nevertheless, these computational tasks are often essential for deriving business value from maintaining databases of customer or patient information. For instance, a hospital may seek performance
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The initial viable FHE scheme was introduced by Gentry in 2009. Preceding schemes were partially homomorphic, capable of either homomorphic addition or homomorphic multiplication exclusively. The Paillier scheme is solely additively homomorphic, allowing only addition operations on ciphertexts. On the other hand, FHE facilitates both homomorphic additions and multiplications, enabling arbitrarily complex computations. Presently, FHE schemes are not practical due to their demanding computational and storage requirements. Ongoing research efforts are dedicated to enhancing the performance of FHE. This section delves into the details of the Paillier scheme and a recent FHE implementation known as the Brakerski-GentryVaikuntanathan (BGV) scheme [3][23].
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