Background: Traditional voting systems face issues like fraud, tampering, and lack of transparency. A blockchain-powered e-voting system can ensure secure, tamper-proof, and transparent elections while maintaining voter anonymity.

Scope of Work:

• Implement a tamper-proof blockchain voting ledger.
• Ensure secure voter authentication and anonymity.
• Enable real-time verification and auditable records.
• Design a user-friendly voting interface.

Expected Outcomes:

• A secure and transparent e-voting system.
• A blockchain-backed voter verification mechanism.
• An auditable and fraud-resistant election framework.

Background: Centralized identity management is vulnerable to data breaches and identity theft. Blockchain-based identity solutions can give users control over their personal information while ensuring privacy and security.

Scope of Work:

• Implement self-sovereign identity management using blockchain.
• Enable selective disclosure of personal data for verification.
• Ensure privacy, security, and compliance with regulations.
• Integrate multi-factor authentication and cryptographic protection.

Expected Outcomes:

• A secure and decentralized digital identity solution.
• A blockchain-based authentication model for enterprises and governments.
• A user-friendly and privacy-enhancing identity verification system.

Background: Counterfeiting, fraud, and lack of traceability are major concerns in supply chains (e.g., pharmaceuticals, food, luxury items). Blockchain can enhance authenticity and tracking throughout the supply chain.

Scope of Work:

• Implement blockchain-based supply chain tracking.
• Enable real-time verification of product authenticity.
• Use smart contracts for automated supplier verification.
• Ensure secure and transparent transaction records.

Expected Outcomes:

• A blockchain-backed supply chain tracking system.
• A real-time anti-counterfeit verification mechanism.
• A transparent and auditable product lifecycle record.

Background: Insurance claim processing is slow, inefficient, and prone to fraud. Smart contracts can automate verification and payout processes while ensuring transparency.

Challenge: Develop a blockchain-based insurance claim system that uses smart contracts to automate claim validation and settlement, reducing fraud and processing delays.

Scope of Work:

• Implement self-executing smart contracts for insurance policies.
• Enable real-time claim verification and fraud detection.
• Automate policy validation and payout processes.
• Ensure secure and immutable records of claims.

Expected Outcomes:

• A secure and fraud-resistant insurance claims system.
• An automated smart contract-based claim settlement model.
• A blockchain-powered insurance fraud prevention tool.

Background: Intellectual property (IP) theft and plagiarism are major concerns in digital content creation. Blockchain can ensure secure registration, verification, and ownership tracking of digital assets.

Challenge: Develop a blockchain-based IP protection platform that allows content creators to securely register, track, and verify ownership of digital assets like patents, music, and artwork.

Scope of Work:

• Implement blockchain-based asset registration for IP protection.
• Enable tamper-proof verification of digital ownership.
• Integrate smart contracts for licensing and royalty management.
• Provide automated copyright enforcement mechanisms.

Expected Outcomes:

• A secure and immutable IP registry.
• A real-time verification system for digital assets.
• A blockchain-powered infringement detection framework.

Background: Cybercriminals use sophisticated techniques to evade detection, making digital forensic investigations challenging. AI-powered forensic tools can analyze cybercrime patterns and assist law enforcement.

Challenge: Develop an AI-driven forensic tool that analyzes network logs, traffic, and digital evidence to detect cybercrime activities and assist investigators.

Scope of Work:

• Implement AI models for cybercrime pattern recognition.
• Automate threat intelligence reporting and evidence analysis.
• Develop a real-time forensic investigation dashboard.
• Ensure secure storage and retrieval of digital evidence.

Expected Outcomes:

• An AI-powered cybercrime detection system.
• A digital forensic investigation dashboard for law enforcement.
• A real-time cyber threat intelligence solution.

Background: Deepfake technology is used for misinformation, fraud, and cybercrimes. AI-based forensic tools can detect manipulated media and prevent misuse.

Challenge: Develop an AI-powered deepfake detection tool that analyzes videos, images, and voice recordings to detect deepfake manipulation.

Scope of Work:

• Implement AI-based deepfake detection algorithms.
• Analyze facial inconsistencies, voice patterns, and metadata.
• Develop a real-time verification dashboard.
• Automate report generation for forensic use.

Expected Outcomes:

• A real-time AI-powered deepfake detection system.
• A media authentication tool to prevent misinformation.
• A forensic deepfake analysis platform.

Background: Data privacy laws require organizations to anonymize sensitive data while preserving analytical utility. AI can enhance automated anonymization techniques.

Challenge: Develop an AI-based data anonymization framework that protects personal data while allowing secure data analytics and GDPR compliance.

Scope of Work:

• Implement AI-driven anonymization models (e.g., differential privacy).
• Enable privacy-preserving data sharing.
• Ensure regulatory compliance with GDPR.
• Automate anonymization for various data types.

Expected Outcomes:

• A privacy-enhancing AI anonymization system.
• A secure and compliant data-sharing framework.
• An automated privacy protection tool for enterprises.

Background: Digital forensic evidence is often subject to tampering and unauthorized modifications, making it difficult to ensure integrity and authenticity in legal investigations. Blockchain technology can provide tamper-proof storage and verification of forensic evidence.

Challenge: Develop a blockchain-based digital evidence authentication system to securely store and verify digital evidence while preventing unauthorized alterations.

Scope of Work:

• Implement blockchain-based digital forensic evidence storage.
• Enable cryptographic verification of evidence integrity.
• Develop a tamper-proof audit trail for legal cases.
• Ensure secure access control mechanisms for investigators.

Expected Outcomes:

• A tamper-proof blockchain system for digital evidence storage.
• A real-time forensic verification tool for law enforcement.
• A transparent and immutable audit trail for court proceedings.

Background: Cybercriminals are constantly developing new malware variants, making traditional security methods ineffective. AI-based automated malware analysis can improve threat detection and response.

Challenge: Develop an AI-powered malware detection and classification system to automate the identification, behavior analysis, and risk assessment of emerging cyber threats.

Scope of Work:

• Implement AI/ML models for real-time malware classification.
• Enable automated malware signature generation and detection.
• Develop a threat intelligence dashboard for security teams.
• Provide incident response recommendations based on analysis.

Expected Outcomes:

• A real-time AI malware classification and analysis system.
• A threat intelligence reporting tool for security experts.
• A malware behavior analysis framework for cybersecurity teams.

Background: Encrypted hard drives and mobile devices present challenges for data recovery and forensic investigations. AI-based forensic tools can help reconstruct lost or corrupted data.

Challenge: Develop an AI-driven forensic data recovery system to extract and reconstruct encrypted or corrupted data from storage devices without compromising integrity.

Scope of Work:

• Implement AI-based data recovery and reconstruction techniques.
• Enable automated decryption for forensic investigations.
• Develop a forensic toolkit for law enforcement agencies.
• Ensure compliance with digital evidence integrity regulations.

Expected Outcomes:

• An AI-powered forensic data recovery system.
• A secure evidence extraction framework for legal investigations.
• A real-time forensic recovery toolkit for law enforcement.

Background: Processing sensitive data on cloud platforms poses security risks. Homomorphic encryption allows computations on encrypted data without decryption, preserving confidentiality.

Challenge: Develop a homomorphic encryption framework to enable secure computations on encrypted data in cloud-based environments.

Scope of Work:

• Implement fully homomorphic encryption (FHE) models.
• Enable privacy-preserving computations on encrypted datasets.
• Develop a secure cloud-based computation framework.
• Ensure compliance with global data privacy regulations.

Expected Outcomes:

• A secure cloud computing framework using homomorphic encryption.
• A privacy-enhanced encrypted data processing model.
• A regulatory-compliant cloud security architecture.

Background: Users’ personal data is often exploited by third parties without consent. A decentralized personal data marketplace enables secure and user-controlled data monetization.

Challenge: Develop a blockchain-powered personal data marketplace where individuals can control, share, and monetize their data securely while ensuring privacy.

Scope of Work:

• Implement decentralized data sharing using blockchain.
• Enable privacy-preserving encryption for sensitive data.
• Develop a consent-based data monetization model.
• Ensure compliance with GDPR and data protection laws.

Expected Outcomes:

• A secure blockchain-based data marketplace.
• A user-controlled data monetization platform.
• A privacy-compliant personal data-sharing system.

Background: Smart cities rely on large-scale data collection, raising concerns about individual privacy. Differential privacy techniques allow governments to analyze urban data while preserving citizen privacy.

Challenge: Develop a differential privacy framework that enables secure data sharing for smart city applications without compromising personal privacy.

Scope of Work:

• Implement differential privacy models for smart city analytics.
• Enable privacy-preserving data aggregation techniques.
• Develop secure and anonymized data-sharing mechanisms.
• Ensure compliance with global privacy regulations.

Expected Outcomes:

• A privacy-enhancing differential privacy system.
• A secure data-sharing model for smart city governance.
• A privacy-preserving analytics framework for urban planning.

Background: Training AI models on centralized data poses privacy risks and regulatory concerns. Federated learning enables AI training across multiple devices without sharing raw data.

Challenge: Develop a federated learning framework that allows multiple organizations to collaboratively train AI models while ensuring data privacy.

Scope of Work:

• Implement privacy-preserving federated learning protocols.
• Enable secure decentralized AI model updates.
• Develop differential privacy and encryption-based data security.
• Ensure compliance with data privacy regulations.

Expected Outcomes:

• A privacy-preserving federated learning system.
• A decentralized AI training framework for enterprises.
• A secure and regulatory-compliant AI model-sharing system.