In the quest to balance the scales between privacy and utility, we’ve gathered insights from nine experts, including Chief R&D Officers and CEOs, on how AI can enhance privacy without losing functionality. From the implementation of differential privacy in research and development to the use of federated learning for protecting user privacy in Gboard, these professionals provide real-world examples where AI has been a game-changer.
- Enhancing Privacy With Differential Privacy
- Anonymized Data for E-Commerce A/B Testing
- AI Anonymization in Financial Data Analysis
- Federated Learning for Content Recommendation
- On-Device AI for Fashion Privacy
- Apple’s Differential Privacy for User Services
- AI Detects Misinformation Without Compromising Privacy
- Tool Balances Anonymization With User Experience
- Federated Learning Protects Gboard User Privacy
Enhancing Privacy With Differential Privacy
AI can enhance privacy while maintaining utility through differential privacy. For instance, instead of analyzing raw user data to recommend products, an e-commerce site can add random noise to the data. Additionally, more rigorous methods can involve using AI algorithms to transform original data into synthetic data that retains the internal information connections used for predictions but completely excludes any real private data. This allows the company to safely share this data with third-party companies, protecting individual privacy while still enabling accurate product recommendations.
Aleksey PshenichniyChief R&D Officer, Elai.io
Anonymized Data for E-Commerce A/B Testing
In one of my recent projects, we utilized AI to enhance user privacy while maintaining high functionality. We implemented a sophisticated AI-based system for A/B testing on an e-commerce site. This system anonymized user data during the testing process, ensuring that individual user identities remained confidential.
For example, instead of tracking users directly, the AI used aggregated data patterns to optimize conversion rates. This approach allowed us to run effective A/B tests on different versions of product pages without accessing or storing personal information. By focusing on aggregated, non-identifiable data, we were able to fine-tune the user experience and increase conversion rates by 20% while strictly adhering to privacy standards.
This solution demonstrates that AI can be a powerful tool for improving privacy. It ensures that user data remains secure while still providing valuable insights for conversion optimization.
Jörg Dennis KrügerAuthor, Expert and Mentor, The Conversion Hacker®
AI Anonymization in Financial Data Analysis
We know how important it is to keep our clients’ information safe, especially since we handle sensitive financial and insurance data.
When I started my company, I wanted our clients to feel confident sharing their personal information with us. We implemented an AI system that anonymizes client data, which means it removes personal details but still allows us to analyze the data effectively.
For example, when we look at market trends or develop financial strategies, the AI lets us use client data without compromising anyone’s privacy. This approach helps us spot trends and patterns without exposing personal information, allowing us to work with partners and analysts without risking sensitive client data.
This balance between privacy and utility has been incredibly beneficial for us at Leverage. It has strengthened client trust, which is something we deeply value. Plus, it ensures we can provide the best service possible without compromising on security.
Rhett StubbendeckCEO & Co-Founder, Leverage Planning
Federated Learning for Content Recommendation
We have utilized AI to enhance privacy through our advanced content recommendation engine. This tool personalizes content suggestions by analyzing user behavior in an aggregated, anonymized manner. We employ federated learning techniques to ensure that data is processed locally on users’ devices, and only the insights are shared, not the raw data itself. This method maintains user privacy while still providing valuable, tailored content recommendations.
One practical example is our AI-driven social media scheduler, which predicts the best times to post based on anonymized user interaction data. This feature helps users maximize their engagement without compromising their privacy. By balancing data privacy and utility, we have built a system that users trust and rely on for effective social media management.
Dinesh AgarwalFounder, CEO, RecurPost
On-Device AI for Fashion Privacy
I designed an AI model for a custom fashion brand where all user data is collected and processed directly on their mobile device, ensuring that only the necessary encrypted data is transmitted to the server, maintaining the highest level of privacy. This approach ensures that customers’ sensitive body-related information never leaves their device or reaches the web portal, providing both security and peace of mind while still delivering a personalized fashion experience.
Amit KansagaraErp Software Consultant, Silent Infotech
Apple’s Differential Privacy for User Services
An example that is worth mentioning is the differential privacy employed by Apple. This technique enables them to gather user information to enhance services such as auto-correction or search results, without directly requesting personal information. Thus, to be more precise, Apple overloads the information with random noise before analyzing it; in this way, each user remains anonymous, but the data as a whole can be useful to the company. It is a good approach to ensure that while using artificial intelligence, people’s privacy is not infringed.
Anup KayasthaFounder, Checker.ai
AI Detects Misinformation Without Compromising Privacy
We can use AI to improve privacy by detecting misinformation versus disinformation in social media. While both can spread false information to the public, the first describes false information spread regardless of intent to deceive, the second spreads false information intentionally and usually for nefarious purposes. Disinformation can be used to spread propaganda by dictatorships to convince their citizens that their country is good and others are evil. More recently, it has also been used to spread false information about COVID-19, climate change, and the Russia-Ukraine conflict.
Using AI to collect Twitter/Facebook posts on these topics and labeling them for misinformation versus disinformation, it is now possible to identify malicious versus benign posts automatically—without identifying who posted them.
Julia HirschbergProfessor of Computer Science, Columbia University
Tool Balances Anonymization With User Experience
AI can be a game-changer in improving privacy without compromising utility. We were particularly concerned about balancing user data protection with the need to provide seamless donation experiences. We implemented an AI-driven data anonymization tool that automatically masks personal identifiers while allowing us to maintain the utility of the data for analytics and personalized donor engagement.
For instance, instead of accessing raw personal data, our team works with anonymized datasets that still offer valuable insights into donor behavior, trends, and preferences. This approach not only safeguards privacy but also enhances the overall user experience by enabling us to make data-driven decisions without putting sensitive information at risk. I believe this balance is crucial in today’s data-driven world, where privacy concerns are at an all-time high.
This AI-driven method ensures that our donors’ privacy is respected, while still allowing us to deliver the high-quality services that we’re known for. By prioritizing privacy through AI, we’ve been able to build greater trust with our users, which I think is the ultimate measure of success in any digital platform.
Raviraj HegdeSvp of Growth & Sales, Donorbox
Federated Learning Protects Gboard User Privacy
One example is the use of federated learning in AI, particularly in mobile apps like Google’s Gboard keyboard. Federated learning allows AI models to improve by learning from data across many devices without the data ever leaving the user’s device.
For instance, Gboard uses federated learning to improve word predictions and suggestions based on how users type, but the data is processed locally on the device. This way, sensitive information never leaves the user’s phone, enhancing privacy without compromising the utility and accuracy of the AI. The AI model gets smarter, but your personal data stays personal.
Bhavik SarkhediCMO, Write Right