Research Terms
This algorithm provides data masking for sensitive data while maintaining research usability. A major challenge in scientific research is lack of data availability due to privacy concerns. Data breaches are estimated to cost the United States $5.85 million in 2014. Many current techniques remove the identity of the data providers, but leave the remaining information unencrypted. While other encryption methods are more secure, they make the encrypted data unusable. Researchers at the University of Florida have developed a data masking method that enables the simultaneous use and masking of patients’ sensitive data. This algorithm will enable researchers to make original sensitive data completely hidden from everyone including data collectors, but still allow many commonly used statistical techniques to produce the same results when applied to the masked data as if they were applied to the original data. It can be integrated into existing technologies including mobile devices, data storage, analytical tools, and data exchange systems.
Data protection software allowing researchers to mine for accurate results while maintaining data confidentiality and patient privacy
This data masking algorithm increases the confidentiality of patients’ information while maintaining the ease of data mining for researchers. The masking is performed in a way that allows many commonly used statistical techniques in medical and social research to produce the same results when applied to the masked data as if they were applied to the original data. The technology integrates matrix encryption, crypto algorithms, cyber-secure protocols, distributed computing, and applied statistical methods for practical privacy-preserving solutions. This approach not only removes patient identifiers, but masks all other data, making original data completely hidden, yet allowing statistical methods to mine such transformed data for correct research results.
This data structure system measures network traffic and stores network contact information in a small memory space. Every day, businesses and individuals are bombarded with everything from spam e-mail to corporate hackers. As networks have gotten faster and information transfer has greatly increased, measuring network traffic has become progressively more important for allocating network resources and ensuring security. In order to measure and track what is entering and leaving your network, it is necessary to contain vast amounts of information in a compact memory space. Unfortunately, today's traffic far exceeds the capabilities of any system currently available. Researchers at the University of Florida have developed a new spread estimator that delivers excellent performance in a tight memory space where all existing estimators no longer work. Not only does it achieve space compactness, but it also operates more efficiently than existing systems.
Network traffic management systems and network security systems
A spread estimator is a software/hardware module on a router that inspects the arrival packets and estimates the spread of each source. The spread is defined as the number of distinct internal hosts that an external host (called a source) has contacted during a measurement period. It has important applications in detecting port scans and distributed denial of service attacks, measuring the infection rate of a worm, assisting resource allocation in a server farm, determining popular web contents for caching, and much more. The main technical challenge is to fit a spread estimator in a fast but small and expensive cache memory in order to operate it at the line speed in a high-speed network. In this invention, researchers at the University of Florida have designed a new spread estimator that delivers good performance in tight memory space where all existing estimators no longer work. The estimator effectively achieves space compactness and also operates more efficiently than anything available on the market today. Its accuracy and efficiency come from a new structure for data storage, called virtual vectors, which allow for the measurement and removal of errors in spread estimation.
