Protect proprietary algorithms while they are being used
Make your algorithms available for others to use without exposing IP.
Privacy and security usually focus on protecting data and algorithms at rest or in transit between collaborating partners. But, equally important is protecting those assets as they are actively used to generate insight.
While data silos are commonplace, the acceleration of machine learning adoption in recent years has led to a new era of siloed proprietary algorithms.
1. MODEL FINGERPRINTING & WATERMARKING
Powerful protection for proprietary models and deep learning algorithms. Learn more.
2. ALGORITHM CONNECTION UTILITY
Securely connect to and use remote datasets. Learn more.
3. MODEL RETRIEVAL
Retrieve models trained with TripleBlind at any time. Learn more.
What Makes A Model Proprietary?
What Makes A Model Proprietary?
The data used for training could be proprietary, or the architecture itself including the design choices, optimizations, and model architecture, could be uniquely derived.
If one company took the time, spent the resources, and applied the expertise to develop a better algorithm, it makes sense that they would hesitate to put the results of that labor out in the open without protections.
What Good Are World-Class Algorithms If They Cannot Be Shared?
Imagine that there are two companies – Company A and Company B.
Company A holds a wealth of credit card transaction data that is subject to data residency restrictions, and therefore siloed within the borders of the country in which it was generated. Company B owns a proprietary machine learning model for detecting fraudulent activity. Company B invested a fair amount of time and budget into developing the model and hesitates to share it with other parties due to risk of IP theft or misuse.
How can these two companies work together for mutual benefit and overcome their respective silos?
Blind Algorithm Tools allow these partners to collaborate securely, privately, and efficiently without the usual risks involved with sharing valuable assets.
Protecting data is just one piece of the puzzle
Blind Algorithm Tools provide powerful capabilities and techniques easily activated to protect proprietary algorithms so they can be safely and remotely leveraged by first- and third-party collaborators.
Tradeoffs between utility and privacy are eliminated. You can have your cake (and eat it too!); the recipe remains a secret.
Maintain full control over your algorithms and their usage
Model Fingerprinting and Watermarking
Model Fingerprinting and Watermarking offer powerful protection for deep learning algorithms – keeping them safe from misuse by counterparties. TripleBlind’s patented solution applies a digital “fingerprint” to deep learning models. If a counterparty were to attempt to extract the labels from your model to train a “new” model of their own, the fingerprint would alert you of a violation of the terms of the agreement.
Connect your assets into the TripleBlind network
Algorithm Connection Utility
The Algorithm Connection Utility enables you to securely connect to and use remote datasets in the TripleBlind ecosystem by registering, or connecting, your algorithm with TripleBlind. Connected algorithms are never uploaded to any TripleBlind server and never visible to TripleBlind.
Instead, they remain stationed behind each owner’s firewall, while collaborating data providers can discover and view metadata about the algorithms in TripleBlind’s online dashboard.
Algorithm owners have full control over who can and cannot discover their algorithms on the TripleBlind web interface, and they can approve or deny any request for usage.
Retrieve algorithms for local use
If you’ve trained an algorithm using our Blind Learning tools, you are welcome to retrieve that model from TripleBlind and use it however you like. Once you export the model into an asset package file on your system, its privacy and security are fully in your control. This option is ideal for those using locally-available data for inferencing or implementing the model for use in a locked-down environment. You can choose whether or not a copy of the algorithm remains discoverable on the web interface and connected to the TripleBlind toolset.
Book A Demo
TripleBlind keeps both data and algorithms in use private and fully computable. To learn more about Blind Learning, or to see it in action, please book a demo!