Technical Glossary

Core Technology Terms

Zero-Knowledge Proofs (zk-SNARKs): Cryptographic protocols that allow one party to prove to another that they know a value without revealing the value itself. zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) enable data verification without exposing underlying information, providing the foundation for privacy-preserving data validation in RONNE's marketplace.

Homomorphic Encryption: Advanced encryption technique that permits computations to be performed on encrypted data without first decrypting it. This enables AI model training on encrypted datasets while preserving data privacy throughout the entire process. Results remain encrypted until decrypted by authorized parties with proper keys.

Smart Contracts: Self-executing contracts with terms directly written into code on blockchain networks. Smart contracts automatically execute agreements when predetermined conditions are met, eliminating need for intermediaries. RONNE uses smart contracts to automate data licensing, payment distribution, and governance processes.

Blockchain: Distributed ledger technology maintaining continuously growing list of records (blocks) linked using cryptography. Each block contains cryptographic hash of previous block, timestamp, and transaction data. Provides immutable, transparent record of all marketplace transactions and governance decisions.

Consensus Mechanism: Protocol used by blockchain networks to achieve agreement on single data value among distributed processes. Ethereum's proof-of-stake consensus ensures network security and transaction validity while reducing energy consumption compared to proof-of-work systems.

Cryptographic Hash Functions: Mathematical algorithms that convert input data of any size into fixed-size string of characters. Hash functions are deterministic, fast to compute, and infeasible to reverse. Used extensively in blockchain for data integrity verification and privacy preservation.

AI/ML Terms

Training Data: Large datasets used to teach machine learning models to make predictions or classifications. High-quality, diverse training data is essential for developing accurate AI systems. Data quality, quantity, and relevance directly impact model performance and generalization capabilities.

Machine Learning Models: Algorithms that automatically improve performance on specific tasks through experience with training data. Models learn patterns and relationships in data to make predictions on new, unseen inputs. Common types include supervised, unsupervised, and reinforcement learning models.

Neural Networks: Computing systems inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers. Deep neural networks with multiple hidden layers enable complex pattern recognition and are foundation of modern artificial intelligence breakthroughs.

Artificial Intelligence (AI): Computer systems capable of performing tasks typically requiring human intelligence, including visual perception, speech recognition, decision-making, and language translation. AI encompasses machine learning, deep learning, natural language processing, and computer vision.

Federated Learning: Machine learning approach where model is trained across multiple decentralized devices or servers without exchanging raw data. Enables collaborative model training while preserving data privacy and reducing bandwidth requirements.

Model Bias: Systematic errors in AI systems resulting from incomplete or unrepresentative training data. Bias can lead to discriminatory outcomes and reduced model accuracy. Diverse, high-quality training data helps mitigate bias and improve fairness.

Inference: Process of using trained machine learning model to make predictions on new data. Inference can be performed in real-time (online) or batch processing (offline) depending on application requirements and computational constraints.

Privacy & Compliance Terms

General Data Protection Regulation (GDPR): European Union regulation governing data protection and privacy for individuals within EU and European Economic Area. GDPR requires explicit consent for data processing, right to data portability, and right to be forgotten. Non-compliance can result in fines up to 4% of annual revenue.

California Consumer Privacy Act (CCPA): California state law enhancing privacy rights and consumer protection for residents of California. Grants consumers rights to know what personal information is collected, delete personal information, opt-out of sale of personal information, and non-discrimination for exercising privacy rights.

Privacy-Preserving Techniques: Methods for analyzing and processing data while protecting individual privacy. Includes differential privacy, secure multi-party computation, homomorphic encryption, and zero-knowledge proofs. Essential for complying with privacy regulations while enabling data utility.

Differential Privacy: Mathematical framework for quantifying and limiting privacy loss when analyzing datasets. Adds calibrated noise to query results to prevent identification of individuals while preserving statistical accuracy of aggregate analysis.

Secure Multi-Party Computation (SMPC): Cryptographic protocols enabling multiple parties to jointly compute functions over their inputs while keeping inputs private. Allows collaborative analysis without revealing sensitive data to other participants.

Data Minimization: Privacy principle requiring collection and processing of only data necessary for specified purposes. Reduces privacy risks by limiting exposure of personal information and complies with GDPR requirements for proportionate data processing.

Anonymization: Process of removing personally identifiable information from datasets to prevent identification of individuals. True anonymization is irreversible and creates data that is no longer considered personal under privacy regulations.