We describe the design of privacy controls and feedback mechanisms for contextual IM, an instant ... more We describe the design of privacy controls and feedback mechanisms for contextual IM, an instant messaging service for disclosing contextual information. We tested our designs on IMBuddy, a contextual IM service we developed that discloses contextual information, including interruptibility, location, and the current window in focus (a proxy for the current task). We deployed our initial design of IMBuddy’s privacy mechanisms for two weeks with ten IM users. We then evaluated a redesigned version for four weeks with fifteen users. Our evaluation indicated that users found our group-level rule-based privacy control intuitive and easy to use. Furthermore, the set of feedback mechanisms provided users with a good awareness of what was disclosed.
The emergence of location-based computing promises new and compelling applications, but raises ve... more The emergence of location-based computing promises new and compelling applications, but raises very real privacy risks. Existing approaches to privacy generally treat people as the entity of interest, often using a fidelity tradeoff to manage the costs and benefits of revealing a person's location. However, these approaches cannot be applied in some applications, as a reduction in precision can render location information useless. This is true of a category of applications that use location data collected from multiple people to infer such information as whether there is a traffic jam on a bridge, whether there are seats available in a nearby coffee shop, when the next bus will arrive, or if a particular conference room is currently empty. We present hitchhiking, a new approach that treats locations as the primary entity of interest. Hitchhiking removes the fidelity tradeoff by preserving the anonymity of reports without reducing the precision of location disclosures. We can therefore support the full functionality of an interesting class of location-based applications without introducing the privacy concerns that would otherwise arise.
The computer and communication systems that office workers currently use tend to interrupt at ina... more The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.
We describe the design of privacy controls and feedback mechanisms for contextual IM, an instant ... more We describe the design of privacy controls and feedback mechanisms for contextual IM, an instant messaging service for disclosing contextual information. We tested our designs on IMBuddy, a contextual IM service we developed that discloses contextual information, including interruptibility, location, and the current window in focus (a proxy for the current task). We deployed our initial design of IMBuddy’s privacy mechanisms for two weeks with ten IM users. We then evaluated a redesigned version for four weeks with fifteen users. Our evaluation indicated that users found our group-level rule-based privacy control intuitive and easy to use. Furthermore, the set of feedback mechanisms provided users with a good awareness of what was disclosed.
The emergence of location-based computing promises new and compelling applications, but raises ve... more The emergence of location-based computing promises new and compelling applications, but raises very real privacy risks. Existing approaches to privacy generally treat people as the entity of interest, often using a fidelity tradeoff to manage the costs and benefits of revealing a person's location. However, these approaches cannot be applied in some applications, as a reduction in precision can render location information useless. This is true of a category of applications that use location data collected from multiple people to infer such information as whether there is a traffic jam on a bridge, whether there are seats available in a nearby coffee shop, when the next bus will arrive, or if a particular conference room is currently empty. We present hitchhiking, a new approach that treats locations as the primary entity of interest. Hitchhiking removes the fidelity tradeoff by preserving the anonymity of reports without reducing the precision of location disclosures. We can therefore support the full functionality of an interesting class of location-based applications without introducing the privacy concerns that would otherwise arise.
The computer and communication systems that office workers currently use tend to interrupt at ina... more The computer and communication systems that office workers currently use tend to interrupt at inappropriate times or unduly demand attention because they have no way to determine when an interruption is appropriate. Sensor-based statistical models of human interruptibility offer a potential solution to this problem. Prior work to examine such models has primarily reported results related to social engagement, but it seems that task engagement is also important. Using an approach developed in our prior work on sensor-based statistical models of human interruptibility, we examine task engagement by studying programmers working on a realistic programming task. After examining many potential sensors, we implement a system to log low-level input events in a development environment. We then automatically extract features from these low-level event logs and build a statistical model of interruptibility. By correctly identifying situations in which programmers are non-interruptible and minimizing cases where the model incorrectly estimates that a programmer is non-interruptible, we can support a reduction in costly interruptions while still allowing systems to convey notifications in a timely manner.
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Papers by Karen Tang