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Fake IDs & Fraudulent KYC: Can Crypto Find Salvation in Swarm-Powered Decentralisation?

Fake IDs & Fraudulent KYC: Can Crypto Find Salvation in Swarm-Powered Decentralisation?

The “OnlyFake” scandal, exposing the ease of bypassing KYC checks with forged IDs, throws a spotlight on the vulnerabilities of centralised verification systems in crypto. But fear not, for decentralisation and Swarm, a leading decentralised data storage and distribution technology, might hold the key to a more secure and empowering future.

Centralised KYC: A Honeycomb for Hackers and Fraudsters

Storing user data on centralised servers creates a honeypot for malicious actors. Deepfakes become potent weapons, exploiting weak verification processes to jeopardise financial security and erode trust. Opaque verifications further exacerbate the issue, leaving users with little control over their data and fostering privacy concerns.

Swarm & Decentralization: Empowering Users, Fortifying Security

Decentralisation offers a paradigm shift. By storing user data on blockchains like Swarm, a distributed and tamper-proof ledger, we eliminate central points of attack. Users regain control through self-sovereign identities, fostering trust and transparency. But how do we verify attributes without exposing sensitive information?

Zero-Knowledge Proofs: Verifying Without Revealing

Zero-knowledge proofs (ZKPs) act as cryptographic shields. They allow individuals to prove they possess certain characteristics (e.g., being above 18) without revealing any underlying data. This guarantees privacy while maintaining the integrity of verification.

A Glimpse into the Future: Secure & Empowering Crypto Identity Management with Swarm

Imagine a world where:

  • Swarm-powered decentralised storage eliminates honeypots, making data breaches a distant memory.
  • ZKPs render deep fakes useless by focusing on attribute verification, not identities.
  • Users hold the reins of their data, fostering trust and transparency within the ecosystem.

Here’s how Swarm and ZKPs could work together:

  1. Store ID data on Swarm: Users upload their encrypted ID documents to the decentralised Swarm network, ensuring data privacy and distribution across multiple nodes.
  2. Zero-knowledge verification: When required, users leverage ZKPs to prove they possess necessary attributes (e.g., age) without revealing the entire document.
  3. Empowered control: Users maintain complete control over their data, deciding who can access specific attributes and revoking access as needed.

The “OnlyFake” incident serves as a stark reminder of the need for change. By embracing Swarm-powered decentralisation and ZKPs, we can create a crypto space where security, privacy, and user empowerment reign supreme.

The question now lies with you: Are you ready to join the movement towards a more secure and empowering crypto future?

Understanding Erasure Coding in Distributed Systems: A Guide to Swarm’s Innovative Approach

Understanding Erasure Coding in Distributed Systems: A Guide to Swarm’s Innovative Approach

Introduction to Data Storage in Distributed Systems

In our increasingly digital world, the importance of effective and secure data storage cannot be overstated. Distributed systems, such as cloud storage networks, represent a significant advancement in this area. These systems distribute data across multiple locations, ensuring accessibility and resilience against failures or data losses. However, this distributed nature also introduces unique challenges in terms of data storage and retrieval. For instance, ensuring data integrity and availability across different nodes in a network becomes more complex. Understanding these challenges is crucial for appreciating the innovative solutions like Swarm’s erasure coding, which are designed to address these specific issues.

Overview of Erasure Coding in Swarm

Imagine you have a jigsaw puzzle, and even if a few pieces are missing, you’re still able to recognise the picture. This analogy aptly describes the principle behind erasure coding, a method used for protecting data in distributed systems like Swarm. In Swarm’s context, erasure coding is not just a safety net for missing data; it’s a strategic approach to ensure data is both secure and optimally stored. This coding technique involves dividing data into chunks, then adding additional ‘parity’ chunks. These extra chunks allow the system to reconstruct the original data even if some chunks are lost or corrupted, much like how you can still make out a picture with a few missing puzzle pieces.

Comparison with Traditional Methods

Traditional data storage methods often rely on redundancy—storing multiple copies of data across different locations. While this approach is straightforward, it’s not the most efficient, especially in terms of storage space and resources. In contrast, erasure coding, as used in systems like Swarm, presents a more sophisticated solution. It strikes an optimal balance between data availability and storage efficiency. By storing additional parity information rather than complete data copies, erasure coding provides a reliable means of data recovery with less overall storage requirement. This efficiency makes it particularly suitable for distributed systems, where resource optimization is key.

Deep Dive into Swarm’s Erasure Coding

Swarm’s implementation of erasure coding through Reed-Solomon coding is a masterclass in data protection. This method, at its core, involves breaking down data into manageable chunks, followed by the creation of additional parity chunks. These extra chunks act as a safety mechanism, allowing for the reconstruction of the original data, should any part be lost or corrupted. It’s a method that mirrors the intricacies of a well-crafted puzzle, where each piece, even if minor, plays a crucial role in the bigger picture. This intricate process not only ensures data integrity but also bolsters the system’s ability to recover from unforeseen data losses.

Real-World Applications in Swarm

In practical scenarios, Swarm’s use of erasure coding is a game-changer, especially in maintaining data integrity and availability. In real-world applications, such as cloud storage services, this translates to an unparalleled reliability for users. Whether it’s safeguarding critical business documents or preserving cherished family photos, Swarm’s system ensures that users’ data remains intact and retrievable, even in the face of partial data losses. This level of reliability and security is what makes Swarm stand out in the crowded field of data storage solutions.

Benefits Specific to Swarm’s Approach

Swarm’s unique approach to erasure coding brings with it a suite of advantages. The enhanced data security that comes from this method is the most prominent, providing a robust shield against data loss. Moreover, the system’s efficiency in data storage is noteworthy; by reducing the need for redundant data copies, it significantly cuts down on storage requirements. This efficiency is not just about saving space – it’s also about optimising resources and reducing costs, making it a highly cost-effective solution for large-scale data storage needs.

Technical Challenges and Solutions

The implementation of erasure coding in Swarm, while beneficial, is not without its complexities. Managing the intricate balance between data accessibility, integrity, and storage efficiency presents a significant challenge. However, Swarm’s sophisticated coding techniques and network management strategies have been meticulously designed to address these issues. By continually refining these strategies, Swarm ensures a seamless and reliable user experience, maintaining its status as a leader in distributed data storage.


Erasure coding in distributed systems like Swarm marks a significant milestone in digital data storage and protection. In an era where data’s value is ever-growing, the importance of technologies like erasure coding cannot be understated – they are essential for the reliability and security of our digital world.

Understanding Decentralised Data Storage Costs on Ethereum Swarm

Understanding Decentralised Data Storage Costs on Ethereum Swarm

In the dynamic world of blockchain technology, Ethereum Swarm stands out as a cornerstone for decentralized data storage and communication. It’s crucial for users and developers in the Ethereum ecosystem to understand the intricacies of storage costs on this platform. This article delves deeper into the various factors affecting these costs, including network size, data size, and the critical role of BZZ tokens in pricing.

What is Ethereum Swarm

Ethereum Swarm is not just a decentralized storage system; it’s an extension of Ethereum‘s vision to build a comprehensive, decentralized internet. It enables data to be stored and distributed across a network of nodes, reducing reliance on centralized servers and mitigating risks like data loss or censorship. Swarm is designed to seamlessly store Ethereum’s dApp data, smart contracts, and user data, ensuring high availability and resistance to outages.

Factors Influencing Storage Costs

Network Size: The cost of data storage on Swarm is significantly influenced by the network’s size. A larger network means more nodes are available to store data, leading to increased redundancy and potentially lower costs due to economies of scale. In contrast, a smaller network might have higher costs due to increased demand for the limited storage space available.

Data Size: The volume of data being stored directly impacts the cost. Larger files require more space and network resources, naturally incurring higher costs. Smaller data sets, however, are less resource-intensive, making them more economical to store.

The Role of BZZ Tokens

BZZ tokens, Swarm’s native cryptocurrency, are fundamental to its operational model. These tokens facilitate transactions within the Swarm network, serving as a form of payment for storage services. Users pay for storage in BZZ, while node operators earn BZZ by providing storage space. This creates a decentralized market for storage, where prices are governed by supply and demand.

The Pricing Mechanism

Swarm’s pricing model is dynamic, adjusting to real-time conditions in the network. Storage costs are calculated based on several factors, including the amount of data, network congestion, and the availability of nodes. This ensures that the pricing is fair, competitive, and reflective of the network’s current state.

Swarm’s Postage Stamps Mechanism

An integral part of understanding data storage in Swarm is its unique “postage stamp” system. This mechanism is crucial for the functioning of the Swarm network and influences storage costs:

  • Concept of Postage Stamps: In Swarm, users must purchase “postage stamps” to upload and store data. These stamps are essentially proof of payment attached to the data being stored, ensuring that the data remains in the network for a predetermined amount of time.
  • Functioning: When a user wants to store data, they buy a postage stamp using BZZ tokens. The price of the stamp depends on the size of the data and the desired storage duration. The data with a valid postage stamp is then accepted and stored by the nodes in the network.
  • Impact on Storage Costs: The cost of postage stamps adds an additional layer to the overall storage costs on Swarm. It’s a pay-as-you-go model where the more data you store and the longer you want it stored, the more postage stamps you need to purchase.

Understanding Swarm’s Cost Per Gigabyte Per Year

Calculating the cost of storing data, such as a gigabyte for a year on Ethereum Swarm, requires an understanding of several dynamic factors:

  • Market Value of BZZ: Since storage costs are paid in BZZ tokens, the market value of BZZ significantly impacts the cost. As the value fluctuates, so does the cost of storage.
  • Network Demand and Supply: Costs vary depending on the balance between available storage space and the demand for storage. Higher demand or limited supply can drive up costs.
  • Data Redundancy and Replication: Swarm ensures data redundancy for reliability, which might affect the cost as more copies of the data are stored across different nodes.

Given these variables, providing an exact figure for the cost per gigabyte per year can be challenging. However, for illustrative purposes, let’s assume a scenario:

Assume that 1 BZZ equals X USD, – you can check the up to date prices here – and the current rate for storing 1 GB of data for a month is Y BZZ – check the up to date Swarm storage price here. Therefore, the cost to store 1 GB of data for a year would be (Y * 12) * X USD. At the time of writing, based on this calculation you’d pay $1.561 for storing one GB of data for a year on Swarm. It’s important to regularly check the latest rates and BZZ value for the most accurate cost estimation.

Comparisons with Other Storage Solutions

When compared to other decentralized storage systems like IPFS (InterPlanetary File System) and Filecoin, Swarm offers a distinct approach. While IPFS focuses on peer-to-peer file sharing and content addressing, Swarm provides more integrated storage solutions specifically designed for the Ethereum ecosystem. Filecoin, with its unique proof-of-storage model, represents another alternative, highlighting the diversity in decentralized storage solutions.

Future Outlook and Scalability

The future of Swarm is closely tied to the broader development of the Ethereum ecosystem. As Ethereum evolves, so too will Swarm, potentially leading to more efficient storage solutions and cost reductions. Key to this evolution will be improvements in scalability and network efficiency, which are expected to impact storage costs positively.


Grasping the nuances of storage costs on Ethereum Swarm is vital for anyone engaged in the Ethereum ecosystem. The cost is influenced by factors like network size, data volume, and the economic model governing BZZ tokens. As Swarm continues to grow and evolve, staying informed about these developments is crucial for developers and users alike.