Valtora - Secure File Management System Using Blockchain, IPFS, and Smart Contracts
Bibliographic record
Abstract
In the contemporary digital landscape, traditional centralized file storage systems present significant vulnerabilities including single points of failure, unauthorized access, and lack of transparent audit mechanisms. This project presents a Decentralized Data Vault system that leverages blockchain technology, distributed storage, and cryptographic security to address these critical challenges. The system integrates IPFS (InterPlanetary File System) for decentralized content-addressed storage, Solana blockchain for immutable transaction logging, and MongoDB for metadata management. When users upload files, they are encrypted and stored on IPFS, generating unique content identifiers (CIDs). These CIDs, along with comprehensive metadata, are cryptographically logged on the Solana blockchain, creating an immutable audit trail ensuring data integrity and non-repudiation. A key innovation is the three-tier permission architecture (read, write, admin) enabling fine-grained access control. Users with read-only permissions can view files through secure browser-based viewers—PDFs rendered using PDF.js and Office documents converted to HTML using Mammoth—with comprehensive download prevention mechanisms including disabled right-click, copy protection, watermarking, and keyboard shortcut blocking. Write permission enables viewing and downloading, while admin permission grants complete file management capabilities. The frontend, built with React and TypeScript, provides an intuitive interface with drag-and-drop upload, real-time progress tracking, and responsive design. The Node.js/Express backend implements RESTful APIs with JWT-based authentication, malware scanning using ClamAV, and email notifications via Nodemailer. Security measures include multi-layer encryption, CORS policies, rate limiting, and comprehensive input validation. The blockchain integration provides immutable logging of all operations, transparent audit trails, and cryptographic proof of ownership. Advanced features include public link generation with expiration times, dynamic permission management, and intelligent file preview systems supporting multiple formats (PDF, DOCX, XLSX, PPTX, images, videos). Real-world applications span multiple industries: legal firms requiring tamper-proof document storage, healthcare organizations needing HIPAA-compliant secure sharing, research institutions protecting intellectual property, government agencies managing classified documents, and financial services securing sensitive information. This system represents a paradigm shift in secure file management, combining trustless blockchain properties, distributed storage resilience, and modern web technologies to create a solution that is simultaneously more secure, transparent, and user-friendly than traditional alternatives. By eliminating central server dependencies and implementing cryptographic verification, the Decentralized Data Vault establishes a scalable framework for next-generation digital collaboration where security, privacy, and user sovereignty are fundamental design principles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".