The advancement of artificial intelligence presents new challenges for safeguarding user data. With AI involved, data collection can go uncontrolled, resulting in colossal information leaks and manipulations.

While regulatory institutions are mute on how to frame data protection in the realm of artificial intelligence, the whole responsibility falls onto the shoulders of app developers. This is their call to undertake all measures available to retain users’ data privacy in apps. 

4 Tactics for Ensuring Data Privacy in AI-Driven Apps

  1. AI alignment
  2. Anonymization
  3. Homomorphic encryption
  4. Dual AI networks

Developers cannot do much, but they can take some measures to ensure data privacy. Here, I share my insights on maintaining data privacy in AI-driven applications based on my experience building AI-based products. 

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Alignment Is Fundamental

AI alignment is similar to teaching a child good manners. Just as you want your child to know good habits from bad, you’d like to teach them your values, principles and basic rules of living in this world. 

The same goes for AI — think of it as your Frankenstein in need of being taught the basics. There is hardly a more fundamental thing than data privacy in our highly digitized world. As data becomes a precious commodity, so badly chased by influential big corporations, it requires special protection. Losing this protection can lead to ongoing data breaches and consequently to personal harm to individuals. 

Instilling data ethics into your AI model is a responsibility, not a romantic notion. 



There are a few ways to encrypt your data during the processing phase. However, one option you do have is anonymization. When you anonymize data, you are essentially stripping away any details that can pinpoint who someone is — think names, addresses and contact info. By anonymizing, you either remove or alter these identifiers so the processor cannot single out the user’s identity.

For example, Person Doe’s name can be automatically altered to a random identifier like User12345, or their address can be generalized to a city or state instead of a specific street address. Thus, the information can still be processed while the sensitive data is kept safe. 

In many modern datasets, anonymization is integrated into the system. An algorithm scans the database for sensitive information, ensuring that they are effectively sterilized before the data is processed, shared or stored. 


Homomorphic Encryption

Many of our known data protection methods, such as encryption and anonymization, focus mainly on how data is stored. However, these technologies are designed to maintain privacy when discussing data retention, and when it comes to processing information, privacy challenges arise. How can you let the AI model process the given information without showing it? That’s tricky. 

Besides anonymizing this data, which is taking out any sensible information, there’s another approach: homomorphic encryption

Homomorphic encryption lets you encrypt data before it gets processed. This way, someone else can work with the data without ever seeing the original info. Picture it like this: you put data inside a lockbox and send it to someone. They can work with the contents inside without ever unlocking it. 

Homomorphic encryption is still a theoretical idea. Time will tell whether it will become a go-to practical solution. In practice, this method is computationally intensive, or in other words, too slow. Anything slow is no friend to dynamic startups fighting for the quick attention span of meticulous users. 

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Melding Two AI Networks

If you’re set on ensuring data privacy no matter what, you could meld two AI networks. One handles the processing, takes the raw data and generates predictions and results. At the same time, the other one plays the supervisory role, preventing the exposure of sensitive data and undertaking safety measures. 

For instance, the supervisory model can employ data anonymization by taking away personally identifiable information or turn to differential privacy by adding noise to the data to ensure the sensitive points are not available to single out. Thus, you get a dual-AI setup, which has you covered for one model’s vulnerabilities. 

While this idea may seem like too much effort, it is still more practical than the homomorphic encryption solution. 

As AI increasingly integrates into our daily lives via apps, data protection concerns are growing. With the legal landscape around AI data protection still uncertain, the responsibility falls on app developers to safeguard user data in mobile applications. While there are not many practical steps to ensure data privacy against AI, methods like alignment, anonymization and dual AI set-up can come in handy for developers when building AI applications. 

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