Private computation is challenging to use for a variety of reasons one of which

Private computation is challenging to use for a

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Private computation is challenging to use for a variety of reasons, one of which has been high computational cost. Our most recent result, however, has reduced this by many orders of magnitude and allows privacy to be added to many context algorithms with essentially no computational overhead (accessible as Berkeley Technical Report UCB/EECS-2006-12 from ). This allows us to compute high-level context information, such as who is involved in an activity and how much (say, as a participation number between 0 and 1) without disclosing when and where the users were actually involved. Private computation provides much stronger privacy protection than anonymization for example, e-mail with sender/receiver removed (anonymization) is hardly protected at all. Private computation requires some rather exotic techniques (zero-knowledge proofs), but we have built a Java toolkit that is available to others who would like to experiment with it. Context-Awareness and Perception Context-awareness and perception are really two sides of the same coin. Context- awareness involves interpreting other cues (besides user input) to figure out what a user wants. Many of these cues will require machine perception (is a user talking about food, is there traffic noise, is the sky overcast?). Conversely, machine perception is a difficult task and it “scales” poorly— as you increase the size of the speech vocabulary or the number of potential images to match for vision, accuracy goes down. The task becomes much easier when you add context data to the recognizer. In our research on face recognition, we were able to use available phone context data (time, place, event history) to improve recognition of faces from camera- phone images. In fact, face “recognition” using context data alone (i.e., predicting who’s in the image without looking at it) was more accurate than a state -of-the-art face recognizer using computer vision. Putting computer vision and context together, though, does much better than either one alone.
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Our work on voice interfaces is attempting to achieve similar gains by adding context data to speech recognition. We think the potential gains are even larger there. But there must be closer coupling between recognizer, the context data, and the application or service built on top of it. That brings us to what is realistically the biggest challenge to contextual and perceptual interfaces: bridging the barriers between the disciplines working on these technologies specifically, HCI, speech recognition, and computer vision. It’s a familiar story when there is a paradigm shift in a technology or market. While there are small communities working on the boundaries, most of the time recognizers are “black boxes” to interface develo pers. Conversely, folks working on recognition rarely pay attention to context or the applications that come later. We’ll make some progress that way, but if we want a revolution, which the market is ready for, then we need to forget tribal allegiances and work together.
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  • Fall '17
  • anishal
  • Personal computer

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