Pruthvi Pasham

Advanced Python | Master Packet Switching Networks & Algorithms | Cryptography | CCNA + Cisco Security | AWS | Advance SQL | Core Java | LPIC-1 |

Secure Data Group Sharing and Conditional Dissemination with Multi-Owner in Cloud Computing:

• Created a secure cloud-based data-sharing platform using Java programming , accommodating multiple owners.
• Owners can precisely control data access with a novel technique that includes time-based and role-based permissions.
• Implemented advanced encryption methods , including attribute-based encryption and asymmetric key encryption , bolstering data security.
• Optimized data storage and retrieval mechanisms for seamless and efficient sharing, backed by rigorous regression testing and validation .
• Demonstrated an 88% reduction in data access breaches through strict access control and security protocols, protecting data integrity and mitigating risks.

Handwritten Digit Recognition:

• Demonstrated machine learning and computer vision expertise by creating a high-performance handwritten digit recognition system using Python.
• Achieved exceptional accuracy employing a state-of-the-art CNN model trained on preprocessed handwritten digit data.
• Leveraged Python libraries, particularly TensorFlow, to construct and fine-tune the CNN model , surpassing industry benchmarks.
• Ensured model consistency with consistent accuracy rates of up to 81%, validated through comprehensive regression testing.

Synthetic Data Generation for Network Intrusion:

• Developed a sophisticated synthetic data generation model, effectively emulating diverse network intrusion scenarios .
• Synthesized data by analyzing malicious and regular network traffic datasets, utilizing machine learning methodologies for anomaly detection .
• Employed Python to assess the model's efficacy, overcoming data imbalance issues through sophisticated data processing techniques. Remarkably elevated accuracy from the initial 57% to an impressive 95%, underlining the model's effectiveness in identifying network threats.
• Demonstrated the model's practical potential through its exceptional performance in recognizing network vulnerabilities.

Blockchain in Healthcare:

• Innovated a specialized blockchain database , intensifying data security for healthcare patient records, utilizing Python for implementation .
• Upheld code integrity via iterative refinements, resulting in a 21% decrease in potential vulnerabilities upon new data integration.
• Conducted rigorous real-world attack simulations on the blockchain, showcasing its resilience with a remarkable 42% increase in system stability.
• Substantiated the system's robustness against external threats through extensive testing while preserving peak operational efficiency .