Primal Pappachan

Pennsylvania State University | Penn State · College of Information Sciences and Technology

Website privacy policies are often lengthy and intricate. Privacy assistants assist in simplifying policies and making them more accessible and user-friendly. The emergence of generative AI (genAI) offers new opportunities to build privacy assistants that can answer users’ questions about privacy policies. However, genAI’s reliability is a concern...

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Prior work on preventing inference control through data dependencies detect and deny queries which may lead to leakage, or only protect against exact reconstruction of the sensitive data. These solutions result in...

We study the problem of answering queries when (part of) the data may be sensitive and should not be leaked to the querier. Simply restricting the computation to non-sensitive part of the data may leak sensitive data through inference based on data dependencies. While inference control from data dependencies during query processing has been studied...

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...