به سمت بیومتریک امن، قابل اعتماد، و افزایش حریم خصوصی در ابر
Toward secure, trusted, and privacy-enhanced biometrics in the cloud
Abstract: Recent changes in pervasive sensing applications require software systems that can address diverse technical, architectural, and human issues. Research on wireless sensor networks has served as technical fundamentals, mobile sensing research has helped solving many architectural problems, and now various human/cultural difficulties in pervasive sensing systems are revealed. We compare two contrasting architectural styles, i.e., the cathedral and the bazaar, and discuss the design of a system that unifies the both. Main challenges in designing such a system include: (1) a large amount of personal data; (2) privacy in sharing them; (3) energy-efficiency on mobile devices. We address them using a distributed network of virtually-private data stores featuring rule-based sharing control and flow-based execution of context inferences. Our performance benchmarks show that the rule processing delay is less than 25 ms in typical usage scenarios, and the flow-based execution saves 38.3% of CPU time as well as 54.3% of memory usage in comparison to a bus-based framework. Our twelve-person user study results indicate participants feel less privacy concerns using the rule-based sharing control. We also discuss an interesting tradeoff between usability and controllability, discovered from the user study. Finally, all source code for this research is readily available online.
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