Distributed Learning Data Validation and Reproducibility Research

Glossary

Term Definition Artificial Intelligence (AI) The ability of a computer system to perform tasks that would normally require human intelligence. Machine Learning A subset of AI in which computer systems learn and improve by analyzing data without being explicitly programmed. Federated Learning A machine learning technique in which multiple devices (such as smartphones) collaborate to train a shared model without sending raw data to a central server. Learning Model An algorithm or statistical model developed from training data that is used to make predictions or decisions. Electronic Data Digital information collected, stored, and processed by computer systems. Local Updates Model updates generated by devices participating in federated learning that reflect learning based on their local data. Learning Modifications Changes made to a learning model to improve its performance based on local updates or other data analysis. Blockchain A decentralized digital ledger that securely and transparently records and verifies transactions. Hash A hash algorithm is used to convert input data of arbitrary length into an output value of fixed length, called a hash value. Hash Value A unique representation of data generated by a hash function that serves as a digital fingerprint. Digital Signature An electronic signature that uses cryptographic techniques to verify the authenticity of an electronic document or message. Reproducibility The ability to replicate the results of a study from its raw data and analytical steps. Noun Identifier An alphanumeric identifier used to uniquely identify a source that generated or used electronic data to generate a result. Device Identifier An alphanumeric identifier used to uniquely identify a specific device. Model Identifier An alphanumeric identifier used to uniquely identify a specific learning model. User Identifier An alphanumeric identifier used to uniquely identify a specific user. Main Blockchain A blockchain that contains one or more sub-blockchains and is used to organize and manage data associated with a specific entity. Sub-blockchain A blockchain that is integrated into the main blockchain and is used to store and track specific subsets of data. Data Validation The process of ensuring that data is accurate, complete, and reliable. Original Version The initial and unaltered version of data created or saved at a specific point in time. Current Version The latest version of data at any given time, which may differ from the original version. Trusted Platform Module (TPM) A hardware security module that provides secure cryptographic operations and storage. Metadata Data "about the data" that provides contextual information about other data. Noun Key An encryption key generated by hashing a noun identifier and used for authentication and data retrieval. Secret Sharing An encryption technique in which secret information is divided into multiple shares and a certain number of shares are required to reconstruct the original secret. Share A portion of the secret information in a secret sharing scheme. Sharing policy Defines the rules and algorithms for how shares are distributed and managed in a secret sharing scheme. Data fingerprint A smaller and unique representation of data generated based on a larger data set for verification and identification purposes. Short answer question

Explain the role of federated learning in machine learning. Federated learning is a machine learning approach that allows multiple devices (such as smartphones) to collaborate to train a shared model without sending the original data to a central server. Each device trains a local copy of the model using its local data and shares model updates (not the original data) with the server. The server then aggregates these updates to improve the global model. This process takes advantage of distributed data while protecting privacy.

How can blockchain technology be used to verify data? Blockchain can verify data by creating a tamper-proof record. Each block of data is linked to the previous block and secured using a cryptographic hash function. This structure makes it very difficult to change any data on the blockchain without being detected. Therefore, blockchain can serve as a trusted source of data, providing proof of its authenticity and integrity.

What is a hash algorithm and what role does it play in data verification? A hash algorithm is a function that converts input data of arbitrary length into an output value of fixed length, called a hash value. Hash values act as a unique fingerprint for data. In data verification, any changes in data can be detected by comparing the hash value of the current data with the stored hash value of its original version.

Explain the difference between "original version" and "current version" in data verification. The original version refers to the initial and unaltered version of the data created or saved at a specific point in time. The current version refers to the latest version of the data at any given time, which may be different from the original version. Data verification aims to determine whether the current version retains the integrity of the original version or whether any unauthorized modifications have occurred.

Describe the purpose of a trusted platform module (TPM) in data security. A trusted platform module (TPM) is a hardware chip that is specialized for performing and storing cryptographic operations. It can generate, store, and protect cryptographic keys and provide features such as secure boot, data encryption, and integrity measurement. In data security, a TPM can help ensure the confidentiality, integrity, and authenticity of data.

What is metadata and how is it used to enhance data records? Metadata is data "about data" that provides contextual information about other data. It can include information such as date, time, location, author, file type, etc. Metadata enhances data records because it provides a deeper understanding of the data, making it easier to understand, organize, and interpret the data.

Explain the purpose of noun keys in distributed data systems. A noun key is a cryptographic key generated by hashing a noun identifier, such as a device ID, model ID, or user ID. It can be used as a secure and compact way to reference and retrieve data associated with a specific noun without storing or transmitting the actual identifier.

How effective is secret sharing in protecting sensitive data? Secret sharing is a cryptographic technique that divides secret information into multiple shares, with a certain number of shares required to reconstruct the original secret. By distributing the shares among different parties, it prevents any one party from accessing the full secret alone. This approach enhances data security because the secret remains secure even if some of the shares are compromised.

Explain the role of data fingerprints in data verification. A data fingerprint is a smaller and unique representation of data generated based on a larger data set. It can be used as a proxy to verify and identify the original data without storing or comparing the entire data set. By comparing the fingerprint of the current data with its original fingerprint, any changes in the data can be effectively detected.

Describe the relationship between the main blockchain and the child blockchains in organizing and managing distributed data. The main blockchain can be thought of as a container that contains one or more sub-blockchains that are used to store and manage specific types of data. The main blockchain provides a high-level view, while the sub-blockchains allow for more detailed and targeted organization of related data. This hierarchical structure enhances scalability and efficiency.

Paper title

Evaluate the effectiveness of blockchain technology in ensuring data integrity and reproducibility in federated learning environments.

Analyze the impact of different hashing algorithms on data verification and security in federated learning.

Explore the benefits and challenges of using trusted platform modules (TPMs) in federated learning to enhance data privacy and security.

Design and implement a blockchain-based system for tracking and verifying data provenance and lineage in federated learning environments.

Study the effectiveness and practical significance of using secret sharing and data fingerprinting techniques to protect sensitive data in federated learning