The problem with encrypted data is that you must decrypt it in order to work with it. By doing so, it’s vulnerable to the very things you were trying to protect it from by encrypting it. There is a ...

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Organizations are starting to take an interest in homomorphic encryption, which allows computation to be performed directly on encrypted data without requiring access to a secret key. While the ...

A startup named Ravel claims breakthroughs in fully homomorphic encryption, a hotly-pursued method for analyzing encrypted data without ever decrypting it. Now imagine another approach: instead of ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...

Regardless of the strength of data’s encryption, more and more potential vulnerabilities surface in data security as more people are granted access to sensitive information. However, a relatively new ...

AI and privacy needn’t be mutually exclusive. After a decade in the labs, homomorphic encryption (HE) is emerging as a top way to help protect data privacy in machine learning (ML) and cloud computing ...

What do you do when you need to perform computations on large data sets while preserving their confidentiality? In other words, you would like to gather analytics, for example, on user data, without ...

Data theft and data loss is an endemic problem on the internet. According to the firm Risk Based Security (via TechRepublic), 2020 alone saw 3,932 publicly disclosed breaches with 37 billion records ...

Craig Gentry is creating an encryption system that could solve the problem keeping many organizations from using cloud computing to analyze and mine data: it’s too much of a security risk to give a ...