ShieldNet
Elevator Pitch: ShieldNet fortifies your mobile app’s brain, protecting it from hackers trying to tamper or steal your smart models. Our cutting-edge security service ensures your app remains secure and your users’ data stays private, making your app not just smart, but also safe.
Concept
Advanced on-device model protection service for mobile applications.
Objective
To provide robust security solutions to protect on-device deep learning models in mobile applications from white-box attack strategies, ensuring the confidentiality and integrity of the models.
Solution
Utilizing a proprietary framework similar to REOM but designed for defense, transforming on-device models into a format that’s resilient to reverse engineering and white-box attacks, while maintaining their performance.
Revenue Model
Subscription-based model for mobile app developers and enterprises, with tiered pricing based on the level of security and number of models protected.
Target Market
Mobile application developers, enterprises with mobile applications in finance, healthcare, and other sensitive sectors requiring high-level model security.
Expansion Plan
Initially focus on popular mobile development platforms and gradually expand to emerging technologies and platforms. Partnership with mobile and cloud security companies for integrated solutions.
Potential Challenges
Keeping up with evolving attack strategies and ensuring minimal impact on model performance. Educating the market on the importance of on-device model security.
Customer Problem
Existing mobile applications’ deep learning models are vulnerable to extraction and manipulation, jeopardizing user data and app integrity.
Regulatory and Ethical Issues
Complying with global data protection regulations (GDPR, CCPA) and ensuring the ethical use of defensive technology without enabling malicious actors.
Disruptiveness
Shifts the focus from traditional network-level security measures to advanced on-device model protection, addressing a critical gap in mobile app security.
Check out our related research summary: here.
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