SecureVision

Elevator Pitch: SecureVision revolutionizes data privacy in the AI domain by offering a cutting-edge solution that guards the visual privacy of your data without hampering the performance of your models. Our unique VisualMixer technology enables businesses and researchers to harness the full potential of their image data, while ensuring compliance with privacy regulations, making your AI initiatives secure, ethical, and effective.

Concept

A privacy-preserving platform for image data used in Deep Neural Networks (DNNs), ensuring data privacy without sacrificing model accuracy.

Objective

To protect the privacy of image data used in DNNs without compromising on the model’s performance.

Solution

Using the VisualMixer framework, SecureVision will offer a service to obfuscate image data by pixel shuffling, following the principles of Visual Feature Entropy (VFE), without adding noise to the data.

Revenue Model

Subscription-based for businesses and per-use fees for individual researchers/developers.

Target Market

Companies and researchers in autonomous driving, medical image analysis, security surveillance, and any field reliant on image data processing.

Expansion Plan

Initially target the healthcare and autonomous driving industries, then expand to other sectors such as security surveillance and social media platforms.

Potential Challenges

Balancing privacy protection strength with model performance, and adapting to different DNN architectures.

Customer Problem

Existing privacy protection techniques for image data compromise either data usability or model accuracy.

Regulatory and Ethical Issues

Compliance with global data protection regulations (GDPR, HIPAA) and ensuring ethical use of the technology to prevent misuse.

Disruptiveness

Provides a novel method for visual privacy protection that maintains high model accuracy, revolutionizing how industries manage and protect confidential and sensitive image data.