New Approach to GDPR: Using Machine Learning to Enhance Privacy

The General Data Protection Regulation (GDPR) will come into effect on the 25th of May. Although this particular law applies to the European Union, it does not only concern businesses registered there: every entity that has EU clients will have to comply with it.

GDPR has no precedent. However, it could become the worldwide gold standard of privacy laws. In such a case, these recommendations may soon extend to those operating without any EU customers, especially in the current context of data breaches and insufficient privacy features.

Security & Privacy Concerns

More people are concerned about privacy than ever before. With the rise of the Internet of Things, it is estimated that 8.4 billion devices are currently connected online, with 49% of the world’s population using the internet. That number is only going to increase.

While that has positive implications, such high levels of connectivity also has a negative side to it. In the light of the ongoing Facebook data privacy scandal, the Equifax breach, and the Uber data breach, people are becoming more aware and concerned about their privacy and security online.

64% of Americans have experienced a data breach personally, and nearly half (49%) feel that their private information is less secure than five years ago. That number grows to 58% in the population aged 50 and above. In this kind of environment, laws similar to GDPR could gain rapid popularity in the US, too.

Key Restrictions Presented by GDPR

The General Data Protection Regulation brings quite a few changes. A major difference is that a business will risk paying fines of up to 4% of annual global turnover, or €20 million.

Businesses will be required to notify their users of a breach within 72 hours, provide access to their data and information on where and why it is processed. Options for erasing user data will have to be provided, amongst other modifications.

Privacy by Design is also introduced, which means that only the data absolutely necessary to carry out duties can be held and processed. It also requires that those needing to process that data have as little access to it as possible.

Machine Learning: Seeing More with Less

As the market for enhanced security and privacy grows, General Data Protection Regulation only adds fuel to this fire. Due to its far stricter regulations, ensuring privacy should become the main concern for any business that has clients from European Union.

Machine learning-driven solutions can provide effective services without exposing PII (personally identifiable information that can lead to learning a person’s identity, such as the name and the surname, or the IP address in combination with the physical address)

Machine Learning models can effectively analyze huge data sets in real time to detect specific patterns, anomalies, and trends. There is no need to analyze every single packet and look into the data portion of the packet (where all the sensitive information and, potentially, PII is being stored).

In addition to that, minimal human interaction is required to supervise it. It works well with GDPR’s requirement to avoid the overabundance of officials dealing with sensitive data. That’s because Machine Learning uses algorithms to analyze patterns in data, therefore minimizing the need for human supervision and PII exposure.

According to GDPR, the person has the “right of explanation.” It’s a right to know what kind of data is used by a business about a person, and why. The anonymization of data along with the necessary explanation of what purpose this collection has can ensure compliance.

Of course, businesses should ensure that no unnecessary data is kept and utilized.  However, the main issue with excessive data keeping is unfair and biased profiling, as well as using it to take advantage of clients. Ultimately, a beneficial service that uses data ethically should have few issues, if any.

Machine learning should be looked at as one of the most viable solutions to the current issues of security, privacy, and end-user protection that we are facing today. In general, people are becoming more aware of the risks that being online may present.

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