Primal PappachanUniversity of California, Irvine | UCI · Department of Computer Science
Primal Pappachan
MS in Computer Science
About
12
Publications
2,225
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93
Citations
Introduction
I am a PhD. student in Computer Science at the University of California, Irvine. I completed my Masters from University of Maryland Baltimore County.
Additional affiliations
January 2013 - August 2014
Position
- Research Assistant
Description
- Worked with Dr. Anupam Joshi and Dr. Tim Finin.
January 2013 - present
August 2011 - present
Education
August 2012 - July 2014
July 2007 - June 2011
Publications
Publications (12)
Current approaches for enforcing Fine Grained Access Control (FGAC) in DBMS do not scale to scenarios when the number of access control policies are in the order of thousands. This paper identifies such a use case in the context of emerging smart spaces wherein systems may be required by legislation, such as Europe’s GDPR and California’s CCPA, to...
This demonstration showcases the SemIoTic middleware [2] which provides inhabitants of an IoT space, as well as developers of applications , with a semantic view of the space. Participants will have an opportunity to see how useful IoT applications can be easily developed focusing on describing what information is needed without having to deal with...
The Internet of Things (IoT) is changing the way we interact with our environment in domains as diverse as
health, transportation, office buildings and our homes. In smart building environments, information captured about the building and its inhabitants will aid in development of services that improve productivity, comfort, social interactions, sa...
With the widespread availability of cheap but powerful mobile devices and high-speed mobile Internet, we are witnessing an unprecedented growth in the number of mobile applications (apps). In this paper, we present a systematic review of mobile apps which use Semantic Web technologies. We analyzed more than 400 papers from proceedings of important...
The number of mobile applications (apps) in major app stores exceeded one million in 2013. While app stores provide a central point for storing app metadata, they often impose restrictions on the access to this information thus limiting the potential to develop tools to search, recommend, and analyze app information. A few projects have circumvente...
We present Mobipedia, an integrated knowledge base with information about 1 million mobile applications (apps) such as their category , meta-data (author, reviews, rating, release date), permissions and libraries used, and similar apps. The goal of Mobipedia is to integrate unstructured and semi-structured data about mobile apps from publicly avail...
Community Health Workers (CHWs) act as liaisons between health-care providers and patients in underserved or un-served areas. However, the lack of information sharing and training support impedes the effectiveness of CHWs and their ability to correctly diagnose patients. In this paper, we propose and describe a system for mobile and wearable comput...
Wearable computing devices like Google Glass are at the forefront of technological evolution in smart devices. The ubiquitous and oblivious nature of photography using these devices has made people concerned about their privacy in private and public settings. The Face-Block (http://face-block.me/) project protects the privacy of people around Glass...
Capturing, maintaining, and using context information helps mobile applications provide better services and generates data useful in specifying information sharing policies. Obtaining the full benefit of con-text information requires a rich and expressive representation that is grounded in shared semantic models. We summarize some of our past work...
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts them into Privacy-Aware Pictures. These pictures are generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by othe...
Projects
Projects (3)
If you are a Google Glass user, you might have been greeted with concerned looks or raised eyebrows at public places. FaceBlock helps to protect the privacy of people around you by allowing them to specify whether or not to be included in your pictures.
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts them into Privacy-Aware Pictures. These pictures are generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). FaceBlock would automatically generate a mathematical representation of face identifier for this picture. Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by.
FaceBlock is a proof of concept implementation of a system that can create Privacy-Aware Pictures using smart devices. The pervasiveness of Privacy-Aware Pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable technology without being oblivious about the privacy of those around you.
http://face-block.me
