New IPFS-Based Platform Puts IoT Data Sharing—and the Profits From It—Directly Into Users’ Hands
Every heartbeat logged by a smartwatch, every vibration recorded by an industrial sensor and every interaction on a social feed adds to a torrent of information that is, paradoxically, both enormously valuable and remarkably difficult to share. A computer scientist at King Fahd University of Petroleum and Minerals (KFUPM) in Saudi Arabia has now unveiled a platform designed to change that. In a study published on 29 August 2026 in the Journal of Network and Systems Management, Farag Azzedin describes an extensible, customizable and trustworthy data-sharing platform built on the InterPlanetary File System (IPFS), a decentralized storage protocol, and a novel peer-to-peer file-sharing environment. The platform is aimed at the vast data streams generated by the Internet of Things (IoT), allowing providers and consumers to exchange information on their own terms, trace every data source to its origin, earn money for both their data and their code, and keep malicious actors at bay. Preliminary tests suggest the system delivers all of this at an overhead of less than two percent.
The problem Azzedin set out to tackle is one of scale and control. Billions of connected devices—wearables, vehicles, factory sensors, home appliances—produce staggering volumes of data, and extracting value from that torrent depends on sharing mechanisms capable of satisfying demanding requirements for scalability, performance and security at the same time. Over the past decade, researchers have proposed a wave of blockchain-based data-sharing systems to meet this challenge across application domains that range from electronic health records and COVID-19 medical archives to vehicular social networks and inventory management. The appeal is easy to see: a blockchain provides an immutable, tamper-evident ledger that can record who shared what, with whom, and under what conditions, without requiring participants to trust a central broker. Smart contracts can automate the exchange, releasing data only when predefined conditions are met, while the ledger’s permanent history gives every transaction a built-in audit trail. Yet in his analysis, Azzedin identifies a common weakness running through many of these designs—one that has attracted surprisingly little scrutiny even as the systems themselves have multiplied across the literature.
That weakness is rigidity. Most existing blockchain-based data-sharing platforms strictly define how data is transferred between providers and consumers, prescribing a single, fixed delivery pipeline for every exchange. Participants who want to share data differently—perhaps streaming sensor readings continuously rather than transferring files in batches, or combining several sources into a single customized feed—find themselves boxed in by rules they cannot change. The consequences are twofold: flexibility suffers, because users cannot adapt the system to their needs, and compatibility suffers, because platforms that each mandate their own transfer scheme struggle to interoperate with one another. In a field that has poured immense effort into consensus protocols, encryption schemes and access control, the simple question of how users actually want to move their data has often been treated as an afterthought. Azzedin’s platform is, at its core, an attempt to put that question back at the center of the design.
The technical foundation of the new platform is IPFS, a distributed file system that replaces the internet’s familiar location-based addressing with content-based addressing. When a file enters IPFS, it is identified not by the server that stores it but by a cryptographic hash—a unique digital fingerprint computed from the file’s contents. Anyone requesting that content can receive it from any node in the network holding a copy, and the fingerprint makes tampering self-evident: alter a single byte and the hash changes entirely. Files are typically split into blocks organized as a directed acyclic graph, allowing efficient retrieval and verification of large objects, while identical content is automatically deduplicated across the network—a valuable property when IoT devices emit streams of near-identical sensor readings. The network locates content through a distributed hash table, so there is no central directory to attack, censor or knock offline. Azzedin weaves IPFS together with a peer-to-peer file-sharing environment of his own design, producing a platform in which data sources remain traceable from origin to consumer while the method of delivery becomes, for the first time, customizable.
That customizability flows from a deliberate act of abstraction. Users of the platform define their data in abstract terms—describing what the data is rather than dictating how it must be delivered—and the platform decouples that definition from the mechanics of transfer. This separation invites innovation: providers can experiment with new sharing methods, from one-shot downloads to continuous subscription feeds, without re-engineering the system for each new idea. Crucially, the platform treats those methods as assets in their own right and offers monetization opportunities for users’ code. A developer who devises a clever, efficient way to deliver sensor data does not have to give that code away; the delivery method itself can become a product. The result is a two-sided marketplace in which both the raw material—data—and the machinery that moves it can be traded, turning the plumbing of data sharing into a source of income.
The payment side of the system is equally inventive. Azzedin proposes a decentralized, blockchain-agnostic approach to monetizing data sources that relies on symmetric key encryption, the classical cryptographic scheme in which the same secret key both locks and unlocks information. In essence, a provider encrypts a data source with a symmetric key and makes the encrypted content available through the platform; the key itself is released to a consumer only once payment has been confirmed. Because the scheme is blockchain-agnostic, it is not bound to any single cryptocurrency or settlement network—the platform enables users to monetize both their data sources and their delivery methods through different public blockchains, choosing whichever chain best suits their community and their costs. The choice of symmetric encryption is deliberate as well: symmetric ciphers are computationally far cheaper than public-key cryptography, which matters when large IoT datasets must be encrypted on resource-constrained devices, while the costlier asymmetric operations can be confined to the rare moments when keys are exchanged or payments settled.
Openness, however, cuts both ways. A network in which strangers exchange data directly must also defend itself against those strangers, and peer-to-peer systems have long been haunted by participants who serve corrupted, counterfeit or harmful content. Azzedin, whose earlier work includes taxonomies of reputation assessment in peer-to-peer systems and scalable architectures for modeling trust in IoT environments, builds a trust model directly into the platform’s core. The model evaluates the behavior of participating nodes and uses those evaluations to isolate malicious content, effectively quarantining data from unreliable or dishonest sources before it can propagate, with the stated goal of enhancing user confidence and engagement with social networking services among online users. The measured results are striking: with the trust model in place, the platform’s success rate remained above 80 percent even when half of the nodes in the network were dishonest. For social networking services, where trust between strangers underpins every interaction, that kind of resilience speaks directly to the most persistent objection against decentralized alternatives—the fear that a community without a central gatekeeper cannot protect its own members.
Efficiency, meanwhile, may prove the platform’s most persuasive credential, given the legendary appetite of blockchain systems for computational resources. Azzedin evaluated the platform through two data-sharing use cases and measured overheads of between 0.6 and 1.6 percent in the first and 0.7 percent in the second—a remarkably light toll for a system that simultaneously provides traceable data sources, customizable delivery, encryption-protected monetization and trust-based filtering of malicious content. In practical terms, users pay at most a sliver of their system’s resources for the guarantees they gain, a trade-off few decentralized platforms can claim. The preliminary results from the two scenarios also point toward applications in big data analytics and decision-making, fields that live or die by the efficient movement of heterogeneous data streams from many producers to many consumers. Whether the task is feeding analytics pipelines, supporting smart-city dashboards or letting research teams pool sensor data, the platform is engineered to keep the machinery of exchange light enough to disappear into the background.
The implications reach well beyond the IoT community. Centralized social platforms have long been criticized for harvesting user data and monetizing it behind closed doors, leaving the people who generate that data with neither control nor compensation. A platform in which users define their own data, choose how it is delivered and sell it directly—across multiple public blockchains—sketches a different vision of the social web, one in which value flows back toward its creators. The system’s extensible design also speaks to a chronic fragmentation problem: because most existing platforms prescribe their own rigid transfer rules, today’s data-sharing landscape is a patchwork of incompatible silos. By letting users describe data abstractly and swap delivery mechanisms freely, the platform is built to bridge those silos rather than deepen them. In an era of intensifying scrutiny over data privacy, the design also hands individuals a technical basis for deciding who may access their information and under what terms. And because monetization is blockchain-agnostic, the system can evolve as settlement networks and cryptocurrencies rise and fall, rather than being stranded on a single chain that falls out of favor.
For now, the evidence rests on two carefully constructed use-case scenarios rather than deployment at internet scale, and the study is the work of a single researcher—a reminder of how far a focused idea can travel in computer science. The paper, which appears as volume 34, article 129 of the Journal of Network and Systems Management, moved through a lengthy editorial cycle, from submission in April 2025 to acceptance in July 2026, and was supported by the Interdisciplinary Research Center for Intelligent Secure Systems at KFUPM. Whether the platform will mature into genuine infrastructure for a user-owned data economy is a question only time and larger-scale trials can answer. But as connected devices multiply and pressure mounts on the centralized intermediaries that currently police the world’s data, a design that promises traceable sources, customizable delivery, honest monetization and measurable resilience against dishonest nodes makes a case that few blockchain-based systems can match.
Subject of Research: An extensible, customizable and trustworthy data-sharing platform for IoT and social network data that combines IPFS with a novel peer-to-peer file-sharing environment, enabling traceable data sources, customizable delivery methods, blockchain-agnostic monetization via symmetric key encryption, and an integrated trust model that isolates malicious content.
Subject of Research: Technology and Engineering
Article Title: An Extensible, Customizable and Trustworthy IPFS-Based Social Network Data Sharing Platform
Article References: Azzedin, F. (2026). An Extensible, Customizable and Trustworthy IPFS-Based Social Network Data Sharing Platform. Journal of Network and Systems Management, 34(4), Article 129. https://doi.org/10.1007/s10922-026-10110-0
Image Credits: AI Generated
DOI: 10.1007/s10922-026-10110-0
Keywords: Blockchain, IPFS, Data sharing, Customizability, Trust model, Internet of Things, Peer-to-peer file sharing, Symmetric key encryption, Data monetization, Decentralized storage, Big data analytics, Social networks
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Denise Maddox. (August 30, 2026). IPFS-Powered Platform Enables Trustworthy, Customizable Social Network Data Sharing. Scienmag. https://scienmag.com/ipfs-powered-platform-enables-trustworthy-customizable-social-network-data-sharing/
Denise Maddox. “IPFS-Powered Platform Enables Trustworthy, Customizable Social Network Data Sharing.” Scienmag, 30 August 2026, https://scienmag.com/ipfs-powered-platform-enables-trustworthy-customizable-social-network-data-sharing/. Accessed 30 August 2026.
Denise Maddox. “IPFS-Powered Platform Enables Trustworthy, Customizable Social Network Data Sharing.” Scienmag. August 30, 2026. https://scienmag.com/ipfs-powered-platform-enables-trustworthy-customizable-social-network-data-sharing/
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