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Bloom Filter Look-Up Tables for Private and Secure Distributed Databases in Web3 (Revised Version)

Shlomi Dolev, Ehud Gudes, Daniel Shlomo

TL;DR

This research proposes a decentralized database scheme specifically designed for secure and private key management, and demonstrates the system's capability to securely manage keys, prevent unauthorized access, and ensure privacy, making it a foundational solution for Web3 applications requiring decentralized security.

Abstract

The rapid growth of decentralized systems in theWeb3 ecosystem has introduced numerous challenges, particularly in ensuring data security, privacy, and scalability [3, 8]. These systems rely heavily on distributed architectures, requiring robust mechanisms to manage data and interactions among participants securely. One critical aspect of decentralized systems is key management, which is essential for encrypting files, securing database segments, and enabling private transactions. However, securely managing cryptographic keys in a distributed environment poses significant risks, especially when nodes in the network can be compromised [9]. This research proposes a decentralized database scheme specifically designed for secure and private key management. Our approach ensures that cryptographic keys are not stored explicitly at any location, preventing their discovery even if an attacker gains control of multiple nodes. Instead of traditional storage, keys are encoded and distributed using the BFLUT (Bloom Filter for Private Look-Up Tables) algorithm [7], which enables secure retrieval without direct exposure. The system leverages OrbitDB [4], IPFS [1], and IPNS [10] for decentralized data management, providing robust support for consistency, scalability, and simultaneous updates. By combining these technologies, our scheme enhances both security and privacy while maintaining high performance and reliability. Our findings demonstrate the system's capability to securely manage keys, prevent unauthorized access, and ensure privacy, making it a foundational solution for Web3 applications requiring decentralized security.

Bloom Filter Look-Up Tables for Private and Secure Distributed Databases in Web3 (Revised Version)

TL;DR

This research proposes a decentralized database scheme specifically designed for secure and private key management, and demonstrates the system's capability to securely manage keys, prevent unauthorized access, and ensure privacy, making it a foundational solution for Web3 applications requiring decentralized security.

Abstract

The rapid growth of decentralized systems in theWeb3 ecosystem has introduced numerous challenges, particularly in ensuring data security, privacy, and scalability [3, 8]. These systems rely heavily on distributed architectures, requiring robust mechanisms to manage data and interactions among participants securely. One critical aspect of decentralized systems is key management, which is essential for encrypting files, securing database segments, and enabling private transactions. However, securely managing cryptographic keys in a distributed environment poses significant risks, especially when nodes in the network can be compromised [9]. This research proposes a decentralized database scheme specifically designed for secure and private key management. Our approach ensures that cryptographic keys are not stored explicitly at any location, preventing their discovery even if an attacker gains control of multiple nodes. Instead of traditional storage, keys are encoded and distributed using the BFLUT (Bloom Filter for Private Look-Up Tables) algorithm [7], which enables secure retrieval without direct exposure. The system leverages OrbitDB [4], IPFS [1], and IPNS [10] for decentralized data management, providing robust support for consistency, scalability, and simultaneous updates. By combining these technologies, our scheme enhances both security and privacy while maintaining high performance and reliability. Our findings demonstrate the system's capability to securely manage keys, prevent unauthorized access, and ensure privacy, making it a foundational solution for Web3 applications requiring decentralized security.
Paper Structure (34 sections, 13 equations, 5 figures, 2 tables)

This paper contains 34 sections, 13 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Secure and Effective Key Management Using Secret Sharing Schemes in Cloud Computing
  4. Secure Key Management in Distributed Systems: Challenges and Solutions
  5. Relevance to Our Work
  6. Key Advantages of the Proposed System
  7. High Privacy
  8. Consistency and Concurrent Updates
  9. Efficient and Secure Data Distribution
  10. Security and Resilience in a Decentralized Environment
  11. Methodology
  12. Introduction: System Purpose and General Structure
  13. BFLUT: A Secure Encoding Mechanism
  14. System Implementation
  15. Introduction: A System for Key Management Based on Username and Password
  16. ...and 19 more sections

Figures (5)

  • Figure 1: Get value from BFLUT example from Dolev2022BFLUT
  • Figure 2: Step 1 - System Initialization
  • Figure 3: Step 2 - Insert a new key.
  • Figure 4: False Positive Probability as a Function of $N$.
  • Figure 5: Required $F$ as a function of $P_{FP}$ for fixed $N = 500,000$, $L = 64$, and $U = 4$.