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.
Security tool guarantees privacy in surveillance footage
“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.
Every vote counts for this math student
Graduate student Ashwin Narayan takes off the fall semester to work on an election information database.
3 Questions: The price of privacy in ride-sharing app performance
JTL Urban Mobility Lab researchers examine the effects of protecting user data privacy on the efficiency and service quality of ride-sharing applications.
Helping companies prioritize their cybersecurity investments
By securely aggregating sensitive data from cyber-attacks, the SCRAM platform from MIT CSAIL can quantify an organization’s level of security and suggest what to prioritize.
Safe Paths: A privacy-first approach to contact tracing
A multinational team develops new tools to slow the spread of pandemics.