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In 2019, IBM published a data report that suggested every major organization with an online-based infrastructure has a 25% chance of suffering a data breach. In a world where consumers are increasingly conscious of data security issues, a 25% chance of having your privacy infringed and data stolen is too high for comfort.

As we advance, cybersecurity companies need to change the way they think about their customer’s data.

As we approach a four industrial revolution driven by AI and robotics, new cybersecurity solutions emerge to make people and organizations safer. Included among these solutions are federated machine learning, homomorphic encryption, and differential privacy.

The Trust Factor

Can customers trust big businesses with their private data? Can civilians trust governments? These questions don’t have easy answers. The advantage of entrusting your data to organizations enables them to provide better services, often tailor-made to your interests and demands. This current model is all or nothing. Either an organization gets to use your data to provide you a better user experience, or they don’t. To be clear, the ultimatum here is simple. What do consumers value more, privacy, or innovation?

The jury is still out on that one.

The Dawn of Data Privacy

Data privacy is still a relatively new field; this means there’s so much we don’t understand just yet. Various technologies provide online security and protect your data, but that doesn’t they’re all the same. Some might see different cybersecurity options as competing products and services, but this isn’t necessarily true. Different data security software can work together to provide one platform or air-tight business security in many cases.

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Differential Privacy – The Future of Data Privacy

What if companies could collect and even use consumer data without ever having direct access to it? Different privacy is one new form of data collection and analysis that gradually mitigates privacy issues in data security. Essentially, differential privacy is a system by which companies can use customer data without their employees accessing or manipulating it. One of the major benefits of differential privacy is how it’s helping cutting-edge AI learn new processes and solutions faster than ever before.

Homomorphic Encryption

Holomorphic encryption is similar to differential privacy in that it enables businesses to utilize customer data without allowing them to observe it directly. The difference between homomorphic encryption and differential privacy is that, in this case, the result of data computation and decryptions is itself encrypted.

Federated Machine Learning

Traditionally, machine learning involves centralizing a data set to one machine to learn different tasks and skills using that information. Federated machine learning instead hosts data on one primary system. It then allows other machines to access it to learn from it without letting human intervention access or use this data in any way.

Data Encryption Services in Los Angeles

Data science has already offered so many solutions to the privacy issues we face daily. Still, the matter is that the threat to our data and privacy will always be a concern for consumers. Luckily, one company is providing reliable, state-of-the-art data encryption services in Los Angeles. Visit iQvcloud’s website to find out more.