A Semantic Context-Aware Privacy Model for FaceBlock (original) (raw)

Privacy in a World of Mobile Devices

2014

Our 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.

Privacy Control in Smart Phones Using Semantically Rich Reasoning and Context Modeling

2012

We present our ongoing work on user data and contextual privacy preservation in mobile devices through semantic reasoning. Recent advances in context modeling, tracking and collaborative localization have led to the emergence of a new class of smart phone applications that can access and share embedded sensor data. Unfortunately, this also means significant amount of user context information is now accessible to applications and potentially others, creating serious privacy and security concerns. Mobile OS frameworks like Android lack mechanisms for dynamic privacy control. We show how data flow among applications can be successfully filtered at a much more granular level using semantic web driven technologies that model device location, surroundings, application roles as well as context-dependent information sharing policies.

Data Privacy Ontology for Ubiquitous Computing

International Journal Of Advanced Computer Science And Applications, 2017

Privacy is an ability to understand, choose, and regulate what personal data one shares, with whom, for how long and under what context. Data owners must not lose the rights of ownership, once the data is shared. Privacy decisions have many components that include identity, access granularity, time and context. We propose an ontology based model for data privacy configuration in terms of producer and consumer. Producer is an IP entity who owns data, that is Data owner. Consumer is an IP entity with whom data is shared. We differentiate between consumer and data holder, also and IP entity, which may not have similar access rights as consumer. As we rely on Semantic Web technologies to enable these privacy preferences, our proposed vocabulary is platform independent and can thus be used by any system relying on these technologies. Ideally, producers can specify a set of attributes which consumers must satisfy in order to be granted access to the requested information. Privacy can be configured not only in terms of typical read and edit, but novel attributes like location and time are also included in the proposed ontology.

Preserving User Privacy and Security in Context-Aware Mobile Platforms

Mobile Application Development, Usability, and Security

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

A semantic framework for privacy-aware access control

2008

The issue of privacy is constantly brought to the spotlight since an ever increasing number of services collects and processes personal information from users. In fact, recent advances in mobile communications, location and sensing technologies and data processing are boosting the deployment of context-aware personalized services and the creation of smart environments but, at the same time, they pose a serious risk on individualspsila privacy rights. Being situated in the realms of legal and social studies, the notion of privacy is mainly left, concerning its protection, to legislation and service providerspsila self-regulation by means of privacy policies. However, all laws and codes of conduct are useless without enforcement. Based on this concept, this paper presents a framework conceived on the basis of privacy legislation. It uses a semantic model for the specification of privacy-aware data access rules and a middleware system which mediates between the service providers and the data sources and caters for the enforcement of the regulatory provisions.