Paper Info
Title
Providing architectural support for building privacy-sensitive smart home applications
Abstract
In this thesis, we plan to introduce a new IoT app development framework named Peekaboo, which aims to make it much easier for developers to get the granularity of data they actually need rather than always requesting raw data, while also offering architecture support for building privacy features across all the apps. Peekaboo's architectural design philosophy is to factor out repetitive data pre-processing tasks (e.g., face detection, frequency spectrum extraction) from the cloud side onto a user-controlled hub, and support them as a fixed set of open source, reusable, and chainable operators. These operators pre-process raw data to remove unneeded sensitive user information before the data flow to the cloud (and out of the users' control), thus reducing data egress and many potential privacy risks for users. Further, all the IoT apps built with Peekaboo share a common structure of the chainable operators, making it possible to build consistent privacy features beyond individual apps.
Year
DOI
Venue
2020
10.1145/3410530.3414328
UbiComp/ISWC '20: 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers Virtual Event Mexico September, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-8076-8
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Haojian Jin14610.08
Swarun Kumar239335.36
Jason Hong36706518.75