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Synthk - MODELTRACK

Synthk - MODELTRACK

Keeping Track of Your Data in AI Models

Created on 26th March 2023

Synthk - MODELTRACK

Synthk - MODELTRACK

Keeping Track of Your Data in AI Models

The problem Synthk - MODELTRACK solves

In the era of AI, data is king. With the explosion of models and transformers, companies are collecting and using data more than ever before to train these systems. However, data ownership has become a blurred and complex issue, and users are often left in the dark about how their data is being used.

This lack of transparency can lead to a loss of trust in AI systems and raises concerns about the ethical use of data. It also creates legal and regulatory challenges for companies that use data in their AI models.

At our company, we are solving this problem by implementing hashing techniques to notify users when their data is being used in an AI model. This helps to ensure that data ownership rights are respected and builds transparency and trust in the use of AI.

Our solution provides a win-win situation for both users and companies. Users can feel confident that their data is being used ethically and transparently, while companies can benefit from improved trust and regulatory compliance. By prioritizing data ownership and transparency, we are helping to create a more responsible and ethical approach to AI that benefits everyone.

Challenges we ran into

One of the major challenges we faced while implementing hashing techniques and persistence storage in IPFS was ensuring the accuracy and reliability of the hashing algorithms. As the data being hashed is highly sensitive, any errors or inconsistencies in the hashing process could lead to serious consequences. To overcome this challenge, we had to rigorously test and validate our hashing algorithms, ensuring that they consistently produced accurate results.

Another challenge we faced was ensuring the scalability and performance of our system. As the volume of data being processed can be huge, it was essential that our system could handle the load without compromising on speed or efficiency. To achieve this, we had to optimize our hashing algorithms and implement advanced caching and indexing mechanisms to ensure fast and efficient data access.

Data privacy and security was also a major challenge. As we were dealing with highly sensitive data, it was essential that we implemented robust security measures to prevent any unauthorized access or data breaches. We had to implement end-to-end encryption, access controls, and secure communication protocols to ensure that the data was kept secure at all times.

Tracks Applied (7)

Grand Prizes

We're bringing a useful dapp to the okx ecosystem.

Extra Prizes

Useful tools to continue development

Other Prizes

Interested in the programs

Other Prizes

Interested in the programs

OnePiece Labs

Other Prizes

Interested in the programs
Unstoppable Domains

Unstoppable Domains

UpHonest Scout Community

Interested to help scouting ZK ML proejcts

OnePiece Labs

OPL Incubator Award

Will allow us to continue with the project

OnePiece Labs

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