CLOUD-BASED SYSTEM FOR GENOMIC DATA ANALYSIS

Abstract

ABSTRACT The present invention discloses a cloud-based system for large-scale genomic data analysis that prioritizes privacy and security. It overcomes the limitations of existing solutions by integrating advanced privacy-enhancing technologies like differential privacy and secure multi-party computation. This enables complex computations and data sharing while minimizing the risk of exposing sensitive information. The system also incorporates optimized algorithms for efficient and scalable analysis of large datasets and facilitates collaborative research through a secure framework with controlled data access. Its adaptability to diverse data sources and analysis tools further enhances its utility. This invention provides a powerful solution for unlocking the full potential of genomic data while upholding the highest standards of data protection.

Specification

Description:CLOUD-BASED SYSTEM FOR GENOMIC DATA ANALYSIS

Field of Invention
The present invention relates to the cloud-based system for genomic data analysis. More particularly, a cloud-based system for analyzing large-scale genomic data with enhanced privacy and security.

Background of the Invention
Genomic data is data related to the structure and function of an organism's genome. The genome is all the cellular data an organism needs to grow and function. Genomic data includes information like the sequence of molecules in an organism's genes. Genomic data is collected, stored, and processed by a global network of biologists, geneticists, and data scientists. The field of genomics is expanding, and is expected to generate many exabytes of data over the next decade. The Genomic data analysis decodes the genetic information in an organism's DNA. This is important for advancing knowledge in genetics and biology, and has practical applications in medical diagnostics, drug development, and agricultural research. There are several prior arts that analyses the Genomic data such as:
1. Reference: Y. Chen, B. Peng, X. Jin, J. Li, and X. Li, "GenoGuard: Protecting Genomic Data against Sensitive Information Leakage in Third-Party Cloud Storage," IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 4, pp. 776-789, July/Aug. 2020. • Problem: While GenoGuard addresses privacy concerns in cloud storage, it focuses primarily on storage and not on the computational analysis of genomic data, leaving potential vulnerabilities during processing.
2. Reference: M. Naveed, E. Ayday, E. W. Clayton, and J. Fellay, "Privacy-preserving distributed genome-wide association studies on the cloud," BMC Medical Informatics and Decision Making, vol. 15, no. 1, p. 77, 2015. • Problem: This work focuses on distributed GWAS computation, which may not be suitable for all genomic analysis types, and may have limitations in scalability for very large datasets.
3. Reference: S. Wang, Y. Zhang, W. Lou, and Y. T. Hou, "Privacy-Preserving Compressive Sensing for Cloud-Based Healthcare," IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3687-3698, April 2019. • Problem: Compressive sensing for data reduction may introduce information loss, potentially impacting the accuracy of downstream analysis.
4. Reference: R. Lu, H. Zhu, X. Liu, J. K. Liu, and J. Shao, "Efficient Privacy-Preserving Cloud-based DNA Profile Matching with Fully Homomorphic Encryption," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 4, pp. 1173-1185, July/Aug. 2018. • Problem: While this method provides strong privacy guarantees, fully homomorphic encryption can be computationally expensive, limiting its practicality for large-scale genomic analysis.
Technical Drawback Summary: A typical technical drawback observed in prior art is the trade-off between privacy/security and computational efficiency. While some solutions offer strong privacy protection, they often come at the cost of increased computational overhead, making them less suitable for analyzing massive genomic datasets in a timely and cost-effective manner.

The prior Patents are:
1. Patent: US Patent No. 10,482,655 B2, "System and method for privacy-preserving genomic data sharing and analysis in the cloud," Issued Nov 19, 2019. • Problem: While this patent addresses privacy concerns in cloud-based genomic analysis, it relies on data anonymization techniques, which may not provide sufficient protection against re-identification attacks, especially with advancements in data linkage and inference techniques. • Technical Drawback: Anonymization can lead to information loss, potentially affecting the accuracy and utility of the analysis results.
2. Patent: US Patent No. 10,255,428 B2, "Cloud-based system and method for genomic data management and analysis," Issued Apr 9, 2019. • Problem: This patent primarily focuses on efficient data management and analysis in the cloud, but may lack comprehensive privacy-enhancing techniques specifically designed for sensitive genomic data. • Technical Drawback: Without robust privacy measures, the risk of unauthorized access or data breaches remains a significant concern.
3. Patent: US Patent No. 9,811,415 B2, "Secure and privacy-preserving cloud computing system for genomic data analysis," Issued Nov 7, 2017. • Problem: Although this patent mentions security and privacy, it might depend on traditional encryption methods, which can hinder data sharing and collaborative analysis due to the need for decryption and potential key management issues. • Technical Drawback: Encryption can limit the types of computations performed directly on the encrypted data, potentially requiring data to be decrypted for certain analyses, increasing the risk of exposure.
4. Patent: US Patent Application No. 2019/0242837 A1, "System and method for secure cloud-based genome analysis," Published Aug 8, 2019. • Problem: This application focuses on secure computation techniques but might involve complex cryptographic protocols that can introduce significant computational overhead, affecting the scalability and efficiency of large-scale genomic analysis. • Technical Drawback: Complex cryptographic protocols can increase the system's complexity and require specialized expertise for implementation and maintenance.
Summary of Technical Drawbacks: The primary technical drawback observed in these prior art patents is balancing robust privacy/security measures and maintaining computational efficiency and usability for large-scale genomic data analysis in the cloud. Some solutions prioritize privacy but may sacrifice performance or introduce data sharing and collaboration limitations.

The present invention aims to provide a cloud-based system for genomic data analysis that prioritizes security and incorporates advanced privacy-enhancing techniques to enable flexible, collaborative analysis while maintaining computational efficiency. It addresses the shortcomings of existing solutions by striking a better balance between privacy, functionality, and usability for large scale genomic research.


Objectives of the Invention

The prime objective of the present invention is to provide a cloud-based system for analyzing large-scale genomic data.

Another object of this invention is to provide the cloud-based system for analyzing large-scale genomic data with enhanced privacy and security.

Another objective of the present invention is to provide the cloud-based system for analyzing large-scale genomic data having a robust and comprehensive privacy framework, minimizing the risk of unauthorized access or reidentification of individuals.

Another objective of the present invention is to provide the cloud-based system for analyzing large-scale genomic data enable efficient and scalable analysis of large-scale genomic datasets, ensuring that privacy-enhancing measures do not hinder the system's performance or usability.

Yet another object of this invention is to provide the cloud-based system for analyzing large-scale genomic data that facilitate collaborative research and data sharing among researchers while maintaining robust privacy guarantees.

These and other objects of the present invention will be apparent from the drawings and descriptions herein. Every object of the invention is attained by at least one embodiment of the present invention.
Summary of the Invention
In one aspect of the present invention provides the cloud-based system for analyzing large-scale genomic data with enhanced privacy and security by integrating advanced privacy-enhancing technologies, such as differential privacy and secure multi-party computation, into the core of its cloud-based system, this allows for complex computations and data sharing while minimizing the risk of exposing sensitive individual level genomic information.
In one of the aspects, in the present invention, the system employs optimized algorithms and computational techniques to ensure that privacy-enhancing measures do not significantly impact the system's performance and scalability, this allows researchers to conduct large-scale genomic analyses in a timely and cost-effectively.
In one of the aspects, in the present invention, the system supports a flexible framework that enables researchers to collaborate and share data securely, this may involve mechanisms for controlled data access, secure data-sharing protocols, and privacy preserving collaborative analysis tools.

Brief Description of Drawings
The drawings described herein are for illustrative purposes only of selected embodiments and not all possible implementations, and are not intended to limit the scope of the present disclosure. Further objectives and advantages of this invention will be more apparent from the ensuing description when read in conjunction with the accompanying drawing and wherein:
Figure 1 illustrates the Genomic Data system: Module Flow according to the preferred embodiment of the present invention.
Figure 2 illustrates the Data flow for the Genomic Data system and Diagram illustrating the high security layers within the Genomic Data system according to an embodiment of the present invention.
Figure 3 illustrates the SMPC (Secure Multi-Party Computation) Flow diagram according to an embodiment of the present invention.
Figure 4 illustrates the SMPC Flow with Security Protocols according to an embodiment of the present invention.
Figure 5 illustrates the Data Integrity Check Flow according to an embodiment of the present invention.
Figure 6 illustrates the Backup and Recovery Process according to an embodiment of the present invention.
Figure 7 illustrates the Audit Log Process Flow according to an embodiment of the present invention.

DETAIL DESCRIPTION OF INVENTION
Unless the context requires otherwise, throughout the specification which follow, the word "comprise" and variations thereof, such as, "comprises" and "comprising" are to be construed in an open, inclusive sense that is as "including, but not limited to".

In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.

Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the content clearly dictates otherwise. It should also be noted that the term "or" is generally employed in its sense including "and/or" unless the content clearly dictates otherwise.

The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
The headings and abstract of the invention provided herein are for convenience only and do not interpret the scope or meaning of the embodiments. Reference will now be made in detail to the exemplary embodiments of the present invention.
The present invention discloses a cloud-based large-scale system for genomic data analysis that prioritizes privacy and security. It overcomes the limitations of existing solutions by integrating advanced privacy-enhancing technologies like differential privacy and secure multi-party computation.

In describing the preferred embodiment of the present invention, reference will be made herein to like numerals refer to like features of the invention.

According to preferred embodiment of the invention, referring to Figure 1, the cloud-based system for genomic data analysis illustrates the hierarchical structure of the platform's components, starting with the data upload and preprocessing stage. Data is uploaded through an interface, validated, encrypted, and anonymized before advanced privacy measures like secure multi-party computation (SMPC), differential privacy, and homomorphic encryption are applied. The encrypted data is then stored in the cloud with role-based access control (RBAC) and multi-factor authentication for enhanced security. A collaboration and sharing framework allow for secure data sharing among users, with the results encrypted for output and logged through an auditing system. Compliance and regulatory support, intrusion detection, and data breach prevention fortify the system. The system also supports API integration and data import/export and offers a backup and recovery system to ensure data integrity. Finally, users can access the system via a user interface with customizable workflows.

According to another embodiment of the invention, the cloud-based system for genomic data analysis is divide into ten main modules:
Module1: Data Ingestion and Preprocessing Module;
Module 2: Advanced Privacy-Enhancing Technologies;
Module 3: Data Storage and Management;
Module 4: Genomic Data Analysis Engine;
Module 5: Collaboration and Sharing Framework;
Module 6: Results and Reporting Module;
Module 7: Compliance and Regulatory Support;
Module 8: User Interface and Access;
Module 9: API Integration System;
Module 10: Data Integrity and Backup.

According to another embodiment of the invention, the cloud-based system for genomic data analysis works in the following steps:
Step 1: Data Ingestion and Preprocessing Module:
1.1. Data Upload Interface A user-friendly interface for uploading raw genomic data to the cloud platform;
1.2. Data Validation System Mechanisms for ensuring that genomic data conforms to required formats and standards;
1.3. Data Encryption Module Encrypts genomic data immediately upon upload using industry-standard encryption algorithms (e.g., AES-256);
1.4. Data Anonymization Tool Tools for anonymizing sensitive personal information linked to genomic data to prevent reidentification.
Step 2: Advanced Privacy-Enhancing Technologies:
2.1. Differential Privacy Layer: A system that adds controlled noise to datasets to ensure privacy while enabling data analysis;
2.2. Secure Multi-Party Computation (SMPC) Engine: A computational framework enabling joint analysis of genomic data from multiple parties without revealing the raw data;
2.3. Homomorphic Encryption Engine: Allows computations on encrypted data without decryption, enhancing privacy.

Step 3: Data Storage and Management:
3.1. Cloud-based Encrypted Storage: Distributed, redundant storage that securely holds encrypted genomic data;
3.2. Role-Based Access Control (RBAC): A system for restricting access based on the roles of individual users, ensuring authorized access only.
3.3. Multi-Factor Authentication (MFA): A security feature requiring multiple forms of verification to access the system.
3.4. Key Management System (KMS): Securely manages encryption and decryption keys, reducing risks related to key leakage or compromise.

Step 4: Genomic Data Analysis Engine:
4.1. Parallel Computing Framework: Infrastructure (e.g., Hadoop, Spark) for processing large-scale genomic datasets in parallel to enhance efficiency;
4.2. Privacy-Preserving Machine Learning Models: Machine learning algorithms that process genomic data while preserving individual privacy (e.g., federated learning models);
4.3. Algorithm Optimization System: Optimized algorithms designed to improve the speed and accuracy of genomic data analysis (e.g., genome-wide association studies, variant calling).

Step 5: Collaboration and Sharing Framework:
5.1. Secure Data Sharing Protocols: Mechanisms allowing secure data sharing between researchers while maintaining privacy (e.g., cryptographic sharing);
5.2. Privacy-Preserving Collaborative Workflows: Tools that allow multiple researchers to collaborate on genomic data without exposing individual level sensitive data.

Step 6: Results and Reporting Module:
6.1. Encrypted Output Generator: A system for encrypting the results of genomic data analysis before transmission or storage;
6.2. Privacy-Preserving Data Visualization Tools: Tools for visualizing aggregate genomic data results while ensuring no individual-level data is exposed;
6.3. Auditing and Logging System: A secure system that tracks user actions and accesses, maintaining transparency and accountability for all operations.

Step 7: Compliance and Regulatory Support:
7.1. Compliance Framework: Built-in mechanisms for ensuring compliance with data protection regulations (e.g., HIPAA, GDPR);
7.2. Intrusion Detection and Data Breach Prevention System: Advanced security measures such as anomaly detection and real-time monitoring to prevent unauthorized access or data breaches.

Step 8: User Interface and Access:
8.1. Dashboard Interface: A graphical user interface (GUI) allows users to monitor, manage, and analyze genomic data securely;
8.2. Customizable Workflows: Tools allowing users to customize analysis pipelines and workflows according to their specific research requirements.

Step 9: API Integration System:
9.1. API (Application Programming Interface): A system for allowing third-party applications and tools to integrate with the platform while ensuring secure data exchange;
9.2. Data Import/Export Functions: Mechanisms for securely importing and exporting data to and from external databases or systems.

Step 10: Data Integrity and Backup:
10.1. Data Integrity Checker: A system for verifying that genomic data has not been tampered with during storage or analysis;
10.2. Backup and Recovery System: Redundant backup storage and recovery mechanisms in case of data loss or system failure.
The above steps work together to form the foundation of the cloud-based system for genomic data analysis, emphasizing privacy, security, scalability, and usability. They all interact in a synergistic manner to deliver the advance features of the invention.

According to another embodiment of the invention, referring to Figure 2, the Data Flow and Security Layers for the Genomic Data system illustrates the comprehensive steps and security layers involved in processing genomic data within the system.
The process begins with data ingestion and validation, followed by data encryption and anonymization, applying advanced privacy layers like secure multi-party computation (SMPC) and differential privacy. The data is stored securely using encrypted cloud-based storage, governed by role-based access control (RBAC) and protected by multi-factor authentication. The data analysis engine operates under strict security protocols, ensuring that access is controlled, while intrusion detection and data breach prevention systems actively monitor for threats. Following analysis, results are encrypted, and reporting is generated, with an additional layer of data integrity checking before any backups or API integration for external system interaction. The system also ensures data recovery through a robust backup and recovery system. Together, these processes create a high-security framework that protects sensitive genomic data throughout its lifecycle.

According to another embodiment of the invention, referring to Figure 3, the SMPC (Secure Multi-Party Computation) Flow depicts the process where multiple parties (Party A, Party B, and Party C) securely contribute their private data to a computation without revealing their data to others. The SMPC engine computes all parties' data and produces an encrypted final result. This result is then shared with all participating parties, who can decrypt it using their respective decryption keys, ensuring that the computation's outcome is revealed without compromising the privacy of any party's input data. This enables secure, collaborative computation while preserving data privacy.

According to another embodiment of the invention, referring to Figure 4, the SMPC Flow with Security Protocols illustrates a more secure version of the Secure Multi-Party Computation (SMPC) process. In this flow, each party's private data (Party A, Party B, and Party C) is encrypted before transmission through encrypted communication channels. The encrypted data is used for SMPC computation, and the final computation result is also encrypted. A Data Integrity Check is performed before the respective parties decrypt the results to ensure the final result has not been tampered with. Once authenticated, each party can securely decrypt the computation result. This provides end-to-end security, with encryption and authentication safeguarding the data and the computation process.

According to another embodiment of the invention, referring to Figure 5, the Data Integrity Check Flow outlines ensuring the final encrypted computation result has not been altered or tampered with. Once the result is generated, a Data Integrity Check is initiated, which involves verifying checksums or hashes of the data. This verification process checks for any signs of tampering by comparing the current state of the data with the original data's hash or checksum. The system proceeds to decrypt if the integrity is verified (pass). However, if the integrity check fails, an alert is raised to all parties involved, indicating a potential issue, and the decryption process is halted. This ensures that only verified, unaltered data is processed further.

According to another embodiment of the invention, referring to Figure 6, the Backup and Recovery Process demonstrates the steps to ensure secure data backup and recovery. After data is generated or computed, an encrypted backup is created and stored securely. To verify the integrity of the backup, the system performs a hash or checksum verification. If the integrity check passes, the backup becomes available for on-demand restoration, allowing the system to restore the data when needed and resume normal operations. If the integrity check fails, an alert is triggered, and the system attempts to re-create the backup, ensuring that the data is securely backed up without tampering or corruption. This process ensures data safety and availability for recovery in case of data loss or corruption.

According to another embodiment of the invention, referring to Figure 6, the Audit Log Process Flow outlines the procedure for creating, storing, and reviewing audit logs when a data event occurs. When such an event happens, a log entry is created, capturing the timestamp, user details, operation details, and any data changes. These logs are stored in a secure, tamper-evident storage system to ensure integrity. Logs are cross-checked with integrity checks during the audit review process to verify consistency. The process is complete if the data and logs are verified without discrepancies. However, if differences are found, an alert is triggered for further investigation, ensuring transparency and accountability in data handling.

According to another embodiment of the invention, the system's flexible architecture allows for seamless integration with various data sources and analysis tools, enhancing its adaptability to diverse research needs. This combination of robust privacy measures, optimized computation, and collaborative capabilities sets this invention apart, offering a robust and secure cloud-scale genomic data analysis solution.

According to another embodiment of the invention, the cloud-based system for genomic data analysis has following technical/scientific features setting it apart from prior arts:
1. Integration of Advanced Privacy-Enhancing Technologies: Unlike many prior art solutions that rely primarily on traditional security measures (encryption, access control), this invention distinguishes itself by deeply integrating advanced privacy-enhancing technologies like differential privacy and secure multi-party computation into its core functionality. This allows for complex computations and data sharing while minimizing the risk of exposing sensitive individual-level genomic information, providing higher privacy protection.
2. Optimized Privacy-Preserving Computation: The invention employs optimized algorithms and computational techniques designed explicitly for privacy-preserving genomic data analysis. This ensures that the privacy-enhancing measures do not significantly impact the platform's performance and scalability, enabling efficient analysis of large-scale datasets.
3. Flexible and Secure Collaborative Framework: The system facilitates collaborative research and data sharing through a secure framework incorporating controlled data access, privacy-preserving collaboration tools, and secure data-sharing protocols. This allows multiple researchers to work together on sensitive genomic data without compromising confidentiality, addressing a fundamental limitation of prior art solutions.
4. Adaptability to Diverse Data Sources and Tools: The invention's ability to seamlessly integrate with a wide range of data sources and analysis tools sets it apart from platforms tightly coupled to specific data types or toolsets. This flexibility enhances its utility for researchers working with diverse data and analysis requirements, promoting broader adoption and collaboration.
5. Balancing Privacy and Utility: The invention balances strong privacy guarantees and maintains the utility of genomic data for analysis. By minimizing information loss and enabling complex computations on protected data, it overcomes the limitations of solutions that rely on data anonymization or encryption, which can hinder analysis capabilities.
These technical/scientific features distinguish the invention from prior art technologies, offering a more comprehensive and effective solution for privacy-preserving large-scale genomic data analysis in the cloud.

According to another embodiment of the invention, the cloud-based system for genomic data analysis has probable industrial use or application in the field of pharmaceutical research and drug development. Pharmaceutical companies heavily rely on large-scale genomic data analysis to identify potential drug targets, understand disease mechanisms, and develop personalized therapies. However, the sensitive nature of genomic data poses significant privacy and security challenges. This invention's ability to provide a secure and privacy-preserving cloud-based system for analyzing such data would be invaluable for the pharmaceutical industry. It would enable researchers to collaborate on large-scale genomic studies, share data securely, and conduct complex analyses without compromising patient privacy. This could accelerate drug discovery and development, leading to more targeted and effective treatments for various diseases.

Although a preferred embodiment of the invention has been illustrated and described, it will at once be apparent to those skilled in the art that the invention includes advantages and features over and beyond the specific illustrated construction. Accordingly it is intended that the scope of the invention be limited solely by the scope of the hereinafter appended claims, and not by the foregoing specification, when interpreted in light of the relevant prior art.
, Claims:We Claim;

1. A cloud-based large-scale system for genomic data analysis comprises of 10 main modules, wherein the modules are: Data Ingestion and Preprocessing Module; Advanced Privacy-Enhancing Technologies; Data Storage and Management; Genomic Data Analysis Engine; Collaboration and Sharing Framework; Results and Reporting Module; Compliance and Regulatory Support; User Interface and Access; API Integration System; and Data Integrity and Backup.

2. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein the system works in the following steps:
Step 1: Data Ingestion and Preprocessing Module:
1.1. Data Upload Interface for uploading raw genomic data to the cloud platform;
1.2. Data Validation System Mechanisms for ensuring that genomic data conforms to required formats and standards;
1.3. Data Encryption Module Encrypts genomic data immediately upon upload using industry-standard encryption algorithms;
1.4. Data Anonymization Tool for anonymizing sensitive personal information linked to genomic data to prevent reidentification;
Step 2: Advanced Privacy-Enhancing Technologies:
2.1. Differential Privacy Layer: A system that adds controlled noise to datasets to ensure privacy while enabling data analysis;
2.2. Secure Multi-Party Computation (SMPC) Engine: A computational framework enabling joint analysis of genomic data from multiple parties without revealing the raw data;
2.3. Homomorphic Encryption Engine allows computations on encrypted data without decryption, enhancing privacy;
Step 3: Data Storage and Management:
3.1. Cloud-based Encrypted Storage: Distributed, redundant storage that securely holds encrypted genomic data;
3.2. Role-Based Access Control (RBAC): A system for restricting access based on the roles of individual users, ensuring authorized access only;
3.3. Multi-Factor Authentication (MFA): A security feature requiring multiple forms of verification to access the system;
3.4. Key Management System (KMS): Securely manages encryption and decryption keys, reducing risks related to key leakage or compromise;
Step 4: Genomic Data Analysis Engine:
4.1. Parallel Computing Framework: Infrastructure for processing large-scale genomic datasets in parallel to enhance efficiency;
4.2. Privacy-Preserving Machine Learning Models: Machine learning algorithms that process genomic data while preserving individual privacy;
4.3. Algorithm Optimization System are designed to improve the speed and accuracy of genomic data analysis;
Step 5: Collaboration and Sharing Framework:
5.1. Secure Data Sharing Protocols allows secure data sharing between researchers while maintaining privacy;
5.2. Privacy-Preserving Collaborative Workflows: Tools that allow multiple researchers to collaborate on genomic data without exposing individual level sensitive data;
Step 6: Results and Reporting Module:
6.1. Encrypted Output Generator encrypts the results of genomic data analysis before transmission or storage;
6.2. Privacy-Preserving Data Visualization Tools for visualizing aggregate genomic data results while ensuring no individual-level data is exposed;
6.3. Auditing and Logging System tracks user actions and accesses, maintaining transparency and accountability for all operations;
Step 7: Compliance and Regulatory Support:
7.1. Compliance Framework ensures compliance with data protection regulations;
7.2. Intrusion Detection and Data Breach Prevention System are the advanced security measures to prevent unauthorized access or data breaches;
Step 8: User Interface and Access:
8.1. Dashboard Interface allows users to monitor, manage, and analyse genomic data securely;
8.2. Customizable Workflows are the tools allowing users to customize analysis pipelines and workflows according to their specific research requirements;
Step 9: API Integration System:
9.1. API (Application Programming Interface) for allowing third-party applications and tools to integrate with the platform while ensuring secure data exchange;
9.2. Data Import/Export Functions for securely importing and exporting data to and from external databases or systems;
Step 10: Data Integrity and Backup:
10.1. Data Integrity Checker for verifying that genomic data has not been tampered with during storage or analysis;
10.2. Backup and Recovery System: Redundant backup storage and recovery mechanisms in case of data loss or system failure.

3. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein the Data Integrity Check Flow ensures that the final encrypted computation result has not been altered; once the result is generated, a Data Integrity Check is initiated, which involves verifying checksums of the data, this verification process checks for any signs of tampering by comparing the current state of the data with the original data's checksum, the system proceeds to decrypt if the integrity is verified (pass).
4. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein if the integrity check fails, an alert is raised to all parties involved, indicating a potential issue, and the decryption process is halted, ensuring that only verified, unaltered data is processed further.

5. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein after data is generated, an encrypted backup is created and stored securely; to verify the integrity of the backup, the system performs a checksum verification.

6. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein if the integrity check passes, the backup becomes available for on-demand restoration, allowing the system to restore the data when needed and resume normal operations and,
if the integrity check fails, an alert is triggered, and the system attempts to re-create the backup, ensuring that the data is securely backed up without tampering.

7. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein the Audit Log Process Flow outlines the procedure for creating, storing, and reviewing audit logs when a data event occurs.

8. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein the logs are stored in a secure, tamper-evident storage system to ensure integrity, the Logs are cross-checked with integrity checks during the audit review process to verify consistency, the process is complete if the data and logs are verified without discrepancies.


9. The cloud-based large-scale system for genomic data analysis as claimed in claim 1, wherein if differences are found, an alert is triggered for further investigation, ensuring transparency and accountability in data handling.

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