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Decentralised LLMs


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Decentralised LLMs

Built for the purpose

In today’s digital age, when data privacy has become a major concern for individuals and organisations alike. With the increasing number of data breaches and unauthorised access to personal information, the need for robust data privacy protection measures has never been more pressing. That’s also where Dijets Method Arcs - fully decentralised large language models configured for individual usecases comes into play.

Traditional data privacy protection methods, while important and widely used in LLMs, have certain limitations that make them vulnerable to even the most commonly used prompt injections let alone sophisticated cyber threats. These vulnerabilities include but are not limited to:

  1. Encryption Vulnerabilities: Encryption is commonly used to protect sensitive data by converting it into a coded format that can only be accessed with the correct decryption key. However, encryption can be vulnerable to attacks such as brute-force attacks, where hackers systematically try different decryption keys until they find the correct one. Additionally, encryption can be compromised if the encryption algorithm or key is weak or if the key gets stolen.

  2. Centralised Storage: Many traditional data privacy protection methods rely on centralizsed storage systems, where all data is stored in a single location or server vulnerable to single points of failure. This centralised approach makes it an attractive target for hackers, as breaching the central storage system would grant them access to a large amount of data. A single breach can have severe consequences, potentially compromising the privacy of millions of individuals.

  3. Firewall Limitations: Firewalls are commonly used to protect networks by monitoring and controlling incoming and outgoing network traffic. While firewalls are effective at blocking unauthorized access and known threats, they may not be able to detect and prevent sophisticated and evolving threats. Additionally, firewalls cannot protect against internal threats or attacks that exploit vulnerabilities in the network infrastructure itself.

  4. Human Error and Social Engineering: Despite the advancements in technology, human error remains a significant weakness in data privacy protection. Employees can inadvertently expose sensitive information through mistakes such as falling for phishing scams or using weak passwords. Social engineering attacks, where hackers manipulate individuals into divulging confidential information, can also bypass traditional protection measures.

  5. Limited Control and Transparency: Traditional data privacy protection methods often lack user control and transparency. Individuals may have limited control over how their data is collected, used, and shared by organizations. Additionally, there may be a lack of transparency regarding how organizations handle and protect personal information, making it difficult for individuals to assess the level of privacy and security provided.

Method Arcs

By utilising a decentralized and immutable ledger, blockchain ensures that data cannot be tampered with or altered without leaving a trace.

  1. Enhanced Security: Method Arcs can run on any existing Dijets chains or on a dedicated network deployed solely to service the LLM application(s) essentially turning LLMs into data fortesses protected by the characteristic properties of distributed ledger technology thus making it impossible for hackers to breach the system and access personal information.
  2. Improved Accuracy: LLMs are constantly learning and evolving, which means they can quickly adapt to new threats and identify patterns that may indicate a potential data breach. Method Arcs can communicate across chains so when an LLM identifies a new threat, it can be communicated to other LLMs too.
  3. Transparent and Trustworthy: Method Arcs provide a transparent and auditable record of all data transactions, ensuring that there is no room for manipulation or tampering.
  4. User Empowerment: With blockchain-based LLMs, individuals have greater control over their personal data. They can choose who has access to their information and can easily revoke access if needed.

A world where artificial intelligence can serve basic everyday human tasks is only possible when the data AI uses is actually user-owned and has traceability where each data transaction is backed by cryptographic guarantees and programmatic logic rather than brand guarantees or mere "Trust us, we care about your data" campaigns.

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