September 9, 2024
4 mins
Why Data Masking Doesn't Work in the World of Language Models
When it comes to language models, data masking doesn’t quite cut it. Here's why
Use LLMs to generate synthetic text data
without privacy risks
Secludy makes it easy to have privacy-guarantees
when generating unstructured synthetic text data
Create realistic synthetic datasets that mimic the original data, without risks of leaking PII data
Navigate AI regulations without sacrificing the ability to learn from sensitive data.
Overcome the limitations of existing data de-identification techniques like data masking and redaction.
Ensure your synthetic data assets have formal privacy-guarantees, reducing your compliance overhead.
Secludy is the perfect choice for anyone looking to use LLMs to unlock the potential of their sensitive unstructured text data.
Our platform allows you to generate synthetic data from sensitive datasets that were previously off-limits due to privacy concerns.
Secludy leverages Differentially Private fine-tuning to provide rigorous privacy guarantees when generating synthetic data from private datasets
All downstream synthetic data generation tasks inherit the same privacy protections as the source Differentially Private fine-tuned LLM
Secludy is designed for data science and ML teams, with an intuitive API and configurable privacy parameters. We handle the complex implementation, allowing your team to focus on generating private synthetic data while preserving privacy
We offer comprehensive customer support to ensure your success with Secludy. Our team of experts is available 24/7 to provide guidance, answer questions, and help with troubleshooting any issues you may encounter.
We take security seriously and are committed to protecting your data. You can self-host Secludy to ensure your data is always secure and only accessible by you.
At Secludy, we provide Privacy Enhancing Technology solutions to help you get the most out of your data. Our pricing plans offer a range of features to suit any budget and needs.
Differential Privacy provides formal, provable privacy guarantees for synthetic text generation that masking techniques cannot match, even when using LLMs. Differential Privacy prevents synthetic data from leaking sensitive information, even under extensive attacks. It allows learning from all available data while preserving privacy, overcoming the limitations of masking, which may miss sensitive details and degrade data utility. Importantly, all downstream tasks using the DP fine-tuned model, such as generating synthetic data, inherit the same privacy protections as the DP fine-tuned model.
Differential Privacy enables your team to safely fine-tune LLMs on sensitive data, unlocking powerful AI capabilities without compromising individual privacy. This streamlines compliance for AI projects, reduces data access reviews, and accelerates the development of privacy-preserving AI applications, enhancing your team's ability to innovate with sensitive data.
To explore Secludy's capabilities, simply click "Schedule a Demo" on our website. Fill in your details, and we'll arrange a personalized demonstration to show how our tool can seamlessly integrate into your systems and empower your team with privacy-compliant fine-tuning.
Secludy is designed for data scientists and ML engineers with experience in text data generation and using LLMs. Our API and configurable privacy parameters make implementing DP straightforward for teams familiar with standard synthetic data workflows and LLM usage.
Absolutely! Secludy offers an enterprise-level plan with the option for self-hosting, ensuring full control over your data and tools. Whether it's in your private cloud or on-prem, our solution adapts to your unique security and compliance needs.
If you have any further questions about Secludy, please do not hesitate to contact us.
