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Data Science and Privacy: Defending Sensitive Data in the Age of Analytics

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Read more about author Rohail Abrahani.

When big data began getting corporate attention in the late 2000s, the idea of data privacy was considered lavish and exotic. The public was less concerned about securing their data assets and was only fascinated by the fact that the interconnected digital world would change their lives forever. 

As we soon step into 2023, the concerns about data privacy are growing rapidly. For now, data privacy, data security, and data governance have become the utmost challenges faced by data scientists around the world. If we add artificial intelligence and machine learning to this frame, the problem becomes even more complex.

When it comes to data science, the real confusion is choosing between data transparency and protection. Without gathering gigantic volumes of data and the free flow of information, there is no concept and existence of data science. On the other hand, the more information you collect, the harder it becomes to protect that data.

Protect Your Data Goldmine With 3 Technologies

Data breaches are increasing with the advancements in technology and the increasing reliance of the world on digital technology. Globally, the average cost per breach is around $4.35 million in 2022. This guide to cybersecurity talks about some of the biggest cyber attacks to date.

Due to ever-increasing cyber threats, it is vital to set up a state-of-the-art data protection system to protect and secure the data assets of consumers as well as companies.

Using Homomorphic Encryption

The issue with encrypted data is that you need to decrypt it before using it for computation. But decryption makes your data vulnerable to cyber threats, the reason you decrypted it in the first place. There is a remarkable solution to deal with encrypted data without any need to decrypt it: homomorphic encryption. 

The primary objective of homomorphic encryption is to enable companies and users to leverage computation on encrypted data. It’s like other forms of data encryption that use a public key to encrypt data and allow only the users to gain access to the decrypted version via a matching private key without decrypting it.

Using Federated Machine Learning

With the debut of data silos and the growing need for data privacy, the mainstream centralized methodology of training AI or ML models experienced a range of regulatory and privacy issues. This is because the data had become increasingly vulnerable to breaches as it changed from one location or environment for processing. To address this issue, federated machine learning was introduced. 

Federated learning is an approach in ML that trains an algorithm across a range of decentralized devices or servers using local data samples, without any need for the exchange or transition of data.

Differential Privacy: Beneficial for Data Analysis

Customers are more informed and critical about their data privacy nowadays. Failure to comply with data privacy regulations like GDPR and CCPA can result in big fines. This is where differential privacy comes into play. It serves as a savior for businesses because it helps them to comply with these privacy regulations without limiting their ability to analyze consumer behavior. 

Differential privacy is also instrumental in most AI and ML models to address regulatory compliance – for instance, using sensitive and private medical records or patient data as a training set in the machine learning model.

What Are the Limitations of Full Trust in Data Science? 

Another ongoing debate in the domain of modern computer sciences is whether Data Science is an adversary or an ally with regards to data security and privacy. From one perspective, ethical machine learning models are known to make data and information gathering more secure and regulated, as modern ML models are certainly defending the front lines of cybersecurity. 

On the other hand, the threat actors are also leveraging AI and ML. For instance, the growing trend of AI-based cyberattacks is potentially the biggest challenge to data security around the world. 

Other vital aspects to consider are shady data and human biases that can amplify all types of threats related to data security, which is the polar opposite of what Data Science is struggling to acquire.  

How Can Data Privacy Technologies Be Disambiguated?

There are some ways to curb these issues such as data disambiguating, which involves parsing the collected data by detaching the information from the actual people to which it sounds meaningful. Today, many data privacy and regulatory bodies have made data disambiguation a compulsory requirement. 

From a corporate point of view, this isn’t an ideal approach, as data disambiguation comes amidst some significant limitations: The process is not reversible, and if we parse all vital information from data, it becomes technically more complex to actually use it for any purpose. 

Data generalization is an alternative, where businesses cluster their data into broader segments like demographics and ensure the data can’t be converted back into its meaningful or perceivable format.   

These modern approaches to data security are becoming a new norm across different sensitive niches, but they’re not the absolute solution. This is primarily due to the higher complexity.

Final Words: The Future of Data Privacy

The future of data privacy is not predestined or defined yet, but the general trajectory is pretty obvious. It’s impossible to shift back to the early periods of the 21st century when data security was considered a glamorous aspect. The need for data privacy is now supported by the legislative and regulatory bodies and data privacy roles are in huge demand within different organizations and niches.

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