The information technology and consumer electronics industries could make quantum leaps in terms of improving the power efficiency of electronic systems and devices, says Hewlett-Packard (HP) researcher Parthasarathy Ranganathan. He says that based on the theoretical physical limits of the power costs to transfer information, the energy used by a single handheld device could power a billion desktop computer processors. Multi-use devices, such as smartphones, almost always use more energy than single-use devices such as MP3 players. “This requirement results in designers using the ‘union’ of maximum requirements of all application classes,” Ranganathan says. He suggests that one technique which could be more widely used is “spending energy to save energy.” The idea is to develop new capabilities that lower overall energy usage, even if the new capabilities themselves require additional energy to run. One example would be a program that regularly scans the memory of servers to reclaim portions that have been reserved by programs but are no longer being used. Another suggestion is to examine the power consumption of a system by looking at the entire ensemble of components, rather than focusing on the consumption of individual units.
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Vanderbilt University (VU) researchers have developed an algorithm designed to protect the privacy of medical patients while maintaining researchers’ ability to analyze large amounts of genetic and clinical data. Although patient records are anonymized, they still contain the numerical codes, known as ICD codes, which represent every condition a doctor has detected. As a result, VU professor Bradley Malin says it is possible to follow a specific set of codes backward and identify a person. Malin and his colleagues found that they could identify more than 96 percent of a group of patients based only on their particular set of ICD codes. To make patients more private, the researchers designed an algorithm that searches a database for combinations of ICD codes that distinguish a patient and then substitutes a more general version of the codes to ensure each patient’s altered record is indistinguishable from a certain number of other patients. The researchers tested the algorithm on 2,762 patients and could not identify any of them based on their new ICD codes.
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