Browsing by Author "Pappachan, Primal"
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Item Are Apps Going Semantic? A Systematic Review of Semantic Mobile Applications(CEUR-WS, 2015-10) Yus, Roberto; Pappachan, PrimalWith 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 conferences on Semantic Web and other venues. We give a brief overview of the 36 semantic mobile apps we identified by grouping them based on their specific functionalities. Our results show that usage of Semantic Web technologies on mobile devices is on the rise and there is a need for development of more tools to facilitate this growth.Item Building a Mobile Applications Knowledge Base for the Linked Data Cloud(CEUR-WS, 2015-10-12) Pappachan, Primal; Yus, Roberto; Das, Prajit Kumar; Mehrotra, Sharad; Finin, Tim; Joshi, AnupamThe 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 circumvented these limitations and managed to create a dataset with a substantial number of apps. However, accessing this information, especially for the purpose of an integrated view, is difficult as there is no common standard for publishing data. We present Mobipedia, an effort to gather this information from various sources and publish it as RDF Linked Data. We describe the status of Mobipedia, which currently has information on more than one million apps that has been extracted from a number of unstructured and semi-structured sources. This paper describes the ontology used to model information, the process for fact extraction, and an overview of applications facilitated by Mobipedia.Item Demo: FaceBlock: privacy-aware pictures for google glass(ACM SIGMOBILE, 2014-06-16) Yus, Roberto; Pappachan, Primal; Das, Prajit Kumar; Mena, Eduardo; Joshi, Anupam; Finin, TimFaceBlock 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.Item GenAIPABench: A Benchmark for Generative AI-based Privacy AssistantsHamid, Aamir; Reddy Samidi, Hemanth; Finin, Tim; Pappachan, Primal; Yus, RobertoPrivacy policies inform users about the data management practices of organizations. Yet, their complexity often renders them largely incomprehensible to the average user, necessitating the development of privacy assistants. With the advent of generative AI (genAI) technologies, there is an untapped potential to enhance privacy assistants in answering user queries effectively. However, the reliability of genAI remains a concern due to its propensity for generating incorrect or misleading information. This study introduces GenAIPABench, a novel benchmarking framework designed to evaluate the performance of Generative AI-based Privacy Assistants (GenAIPAs). GenAIPABench comprises: 1) A comprehensive set of questions about an organization's privacy policy and a data protection regulation, along with annotated answers for several organizations and regulations; 2) A robust set of evaluation metrics for assessing the accuracy, relevance, and consistency of the generated responses; and 3) An evaluation tool that generates appropriate prompts to introduce the system to the privacy document and different variations of the privacy questions to evaluate its robustness. We use GenAIPABench to assess the potential of three leading genAI systems in becoming GenAIPAs: ChatGPT, Bard, and Bing AI. Our results demonstrate significant promise in genAI capabilities in the privacy domain while also highlighting challenges in managing complex queries, ensuring consistency, and verifying source accuracy.Item Mobipedia: Mobile Applications Linked Data(CEUR-WS, 2015-10) Pappachan, Primal; Yus, Roberto; Das, Prajit Kumar; Mehrotra, Sharad; Finin, Tim; Joshi, AnupamWe 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 available data sources and publish it as Linked Data using RDF. We describe the extraction process for facts, access mechanisms to the knowledge base, and an overview of applications facilitated by Mobipedia.Item Privacy in a World of Mobile Devices(University of Texas at Dallas, 2014-09-15) Finin, Tim; Joshi, Anupam; Pappachan, Primal; Yus, Roberto; Das, Prajit Kumar; Mena, EduardoOur individual privacy is increasingly at risk in a world full of smart mobile devices. The situation will only get worse with the rise of an Internet of Things. One way to address this problem is through the use of systems that better understand their context and and whose information gathering and sharing behaviors can be controlled or influenced by context-aware policies. We illustrate the the problem and an approach to address it through recent work on FaceBlock, a project that protects the privacy of people around Google Glass users by making pictures taken by the latter, Privacy-Aware. Through sharing of privacy policies, users can choose whether or not to be included in pictures.Item PrivacyLens: A Framework to Collect and Analyze the Landscape of Past, Present, and Future Smart Device Privacy Policies(2023-08-11) Hamid, Aamir; Samidi, Hemanth Reddy; Finin, Tim; Pappachan, Primal; Yus, RobertoAs the adoption of smart devices continues to permeate all aspects of our lives, concerns surrounding user privacy have become more pertinent than ever before. While privacy policies define the data management practices of their manufacturers, previous work has shown that they are rarely read and understood by users. Hence, automatic analysis of privacy policies has been shown to help provide users with appropriate insights. Previous research has extensively analyzed privacy policies of websites, e-commerce, and mobile applications, but privacy policies of smart devices, present some differences and specific challenges such as the difficulty to find and collect them. We present PrivacyLens, a novel framework for discovering and collecting past, present, and future smart device privacy policies and harnessing NLP and ML algorithms to analyze them. PrivacyLens is currently deployed, collecting, analyzing, and publishing insights about privacy policies to assist different stakeholders of smart devices, such as users, policy authors, and regulators. We show several examples of analytical tasks enabled by PrivacyLens, including comparisons of devices per type and manufacturing country, categorization of privacy policies, and impact of data regulations on data practices. At the time of submitting this paper, PrivacyLens had collected and analyzed more than 1,200 privacy policies for 7,300 smart devices.Item Rafiki: A Semantic and Collaborative Approach to Community Health-care in Underserved Areas(IEEE, 2014-10-22) Pappachan, Primal; Yus, Roberto; Joshi, Anupam; Finin, TimCommunity 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 computing devices called Rafiki which assists CHWs in decision making and facilitates collaboration among them. Rafiki can infer possible diseases and treatments by representing the diseases, their symptoms, and patient context in OWL ontologies and by reasoning over this model. The use of semantic representation of data makes it easier to share knowledge related to disease, symptom, diagnosis guidelines, and patient demography, between various personnel involved in health-care (e.g., CHWs, patients, health-care providers). We describe the Rafiki system with the help of a motivating community health-care scenario and present an Android prototype for smart phones and Google Glass.Item A Semantic Context-Aware Privacy Model for FaceBlock(CEUR-WS, 2014-10-19) Pappachan, Primal; Yus, Roberto; Das, Prajit Kumar; Finin, Tim; Mena, Eduardo; Joshi, AnupamWearable 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 users by making pictures taken by the latter, Privacy-Aware. Through sharing of privacy policies, users can choose whether or not to be included in pictures. However, the current privacy model of FaceBlock only permits simple constraints such as allow versus disallow pictures. In this paper, we present an extended context-aware privacy model represented using OWL ontologies and SWRL rules. We also describe use cases of how this model can help FaceBlock to generate Privacy-Aware Pictures depending on context and privacy needs of the user.Item Semantics for Privacy and Shared Context(CEUR Workshop Proceedings, 2014-10-20) Yus, Roberto; Pappachan, Primal; Das, Prajit Kumar; Finin, Tim; Joshi, Anupam; Mena, EduardoCapturing, 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 context information requires a rich and expressive representation that is grounded in shared semantic models. We summarize some of our past work on representing and using context models and briefly describe Triveni, a system for cross-device context discovery and enrichment. Triveni represents context in RDF and OWL and reasons over context models to infer additional information and detect and resolve ambiguities and inconsistencies. A unique feature, its ability to create and manage "contextual groups" of users in an environment, enables their members to share context information using wireless ad-hoc networks. Thus, it enriches the information about a user's context by creating mobile ad hoc knowledge networks.Item SemIoTic: Bridging the Semantic Gap in IoT Spaces(Association for Computing Machinery, 2019-11-13) Almanee, Sumaya; Bouloukakis, Georgios; Jiang, Daokun; Ghayyur, Sameera; Ghosh, Dhrubajyoti; Gupta, Peeyush; Lin, Yiming; Mehrotra, Sharad; Pappachan, Primal; Shin, Eun-Jeong; Venkatasubramanian, Nalini; Wang, Guoxi; Yus, RobertoThis 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 underlying IoT device infrastructure.Item Sieve: A Middleware Approach to Scalable Access Control for Database Management Systems(ACM, 2020-09-14) Pappachan, Primal; Yus, Roberto; Mehrotra, Sharad; Freytag, Johann-ChristophCurrent 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 empower users to specify who may have access to their data and for what purposes. We present Sieve, a layered approach of implementing FGAC in existing DBMSs, that exploits a variety of their features (e.g., UDFs, index usage hints, query explain) to scale to a large number of policies. Given a query, Sieve exploits its context to filter the policies that need to be checked. It also generates guarded expressions that save on evaluation cost by grouping policies and exploit database indices to cut on read cost. Our experimental results demonstrate that existing DBMSs can utilize Sieve to significantly reduce query-time policy evaluation cost. Using Sieve DBMSs can support real-time access control in applications such as emerging smart environments.Item Towards Privacy-Aware Smart Buildings: Capturing, Communicating, and Enforcing Privacy Policies and Preferences(IEEE, 2017-07-17) Pappachan, Primal; Degeling, Martin; Yus, Roberto; Das, Anupam; Bhagavatula, Sruti; Melicher, William; Naeini, Pardis Emami; Zhang, Shikun; Bauer, Lujo; Kobsa, Alfred; Mehrotra, Sharad; Sadeh, Norman M.; Venkatasubramanian, NaliniThe 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, safety, energy savings and more. However, by collecting and sharing information about building's inhabitants and their activities, these services also open the door to privacy risks. In this paper, we introduce a framework where IoT Assistants capture and manage the privacy preferences of their users and communicate them to privacy-aware smart buildings, which enforce them when collecting user data or sharing it with building services. We outline elements necessary to support such interactions and also discuss important privacy policy attributes that need to be captured. This includes looking at attributes necessary to describe - (1) the data collection and sharing practices associated with deployed sensors and services in smart buildings as well as (2) the privacy preferences to help users manage their privacy in such environments.