Technique enables AI on edge devices to keep learning over time
With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.
Accelerating AI tasks while preserving data security
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.
Helping computer vision and language models understand what they see
Researchers use synthetic data to improve a model’s ability to grasp conceptual information, which could enhance automatic captioning and question-answering systems.
A new way to look at data privacy
Researchers create a privacy technique that protects sensitive data while maintaining a machine-learning model’s performance.
MIT researchers devise a way to evaluate cybersecurity methods
The system analyzes the likelihood that an attacker could thwart a certain security scheme to steal secret information.
Two Lincoln Laboratory software products honored with national Excellence in Technology Transfer Awards
Cloud security and video forensics software have been transitioned to end users.
A faster way to preserve privacy online
New research enables users to search for information without revealing their queries, based on a method that is 30 times faster than comparable prior techniques.
In machine learning, synthetic data can offer real performance improvements
Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.
Learning on the edge
A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
Collaborative machine learning that preserves privacy
Researchers increase the accuracy and efficiency of a machine-learning method that safeguards user data.
Thinking like a cyber-attacker to protect user data
Researchers found that an understudied component of computer processors is susceptible to attacks from malicious agents. Then, they developed mitigation mechanisms.
Keeping web-browsing data safe from hackers
Studying a powerful type of cyberattack, researchers identified a flaw in how it’s been analyzed before, then developed new techniques that stop it in its tracks.
Living better with algorithms
Graduate student Sarah Cen explores the interplay between humans and artificial intelligence systems, to help build accountability and trust.
Technique protects privacy when making online recommendations
Researchers devise an efficient protocol to keep a user’s private information secure when algorithms use it to recommend products, songs, or shows.
3 Questions: Designing software for research ethics
PhD candidate Jonathan Zong found a lack of systems that earn and maintain public trust in large-scale online research — so he made one himself.