Membership inference attacks are not successful on all kinds of machine learning tasks. To create an efficient attack model, the adversary must be able to explore the feature space.

Mittal is a pioneer in understanding an emerging vulnerability known as adversarial machine learning. In essence, this type of attack causes AI systems to produce unintended, possibly dangerous ...

Differential privacy is a method for protecting people’s privacy when their data is included in large datasets. Because differential privacy limits how much the machine learning model can depend ...

Data poisoning is a type of attack that involves tampering with and polluting a machine learning model's training data, impacting the model's ability to produce accurate predictions.

Can we use machine-learning-as-service and protect privacy? Written by Robin Harris, Contributor April 1, 2018 at 5:48 p.m. PT How machine learning and AI helps one digital agency create unique ...

SAN FRANCISCO – As companies quickly adopt machine learning systems, cybercriminals are close behind scheming to compromise them. That worries legal experts who say a lack of laws swing open the ...

Contributor Content In 2025, integrating artificial intelligence (AI) and machine learning (ML) into cybersecurity is no longer a futuristic ideal but a functional reality. As cyberattacks grow ...

Gartner predicts $137.4B will be spent on Information Security and Risk Management in 2019, increasing to $175.5B in 2023, reaching a CAGR of 9.1%.

How to Protect Machine Learning Models Defenders can use methods that can prevent, complicate, or detect attacks to protect ML systems. For example, when adding benign strings to a malware file, a ...