Recommendations on the Internet of Things: Requirements, Challenges, and Directions

© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation

Social pervasive systems: The integration of social networks and pervasive systems

Sensor technology embedded in smart mobile devices branded such devices as candidates for building innovative context-aware pervasive applications. On a parallel front, the notable evolution in the shape and form of social networking and their seamless accessibility from mobile devices founded a goldmine of contextual information. Utilizing an ecosystem that combines both mobile smart devices and a big data like environment in the form of social networks allows for the creation of an elitist set of services and applications that merge the two domains. In this paper, and following the footsteps of similar research efforts that attempted to combine both domains, we describe what we label as Social Pervasive Systems that cross-pollinate a mutually influential mobile and social world with opportunities for new breeds of applications. We present herein the evolution of the merger between both worlds for a better understanding. Above and beyond what related work achieved, we present a set of new services and potential applications that emerge from this new blend, and also describe some of the expected challenges such systems will face

Multi-Agent Modeling of Risk-Aware and Privacy-Preserving Recommender Systems

Recent progress in the field of recommender systems has led to increases in the accuracy and significant improvements in the personalization of recommendations. These results are being achieved in general by gathering more user data and generating relevant insights from it. However, user privacy concerns are often underestimated and recommendation risks are not usually addressed. In fact, many users are not sufficiently aware of what data is collected about them and how the data is collected (e.g., whether third parties are collecting and selling their personal information). Research in the area of recommender systems should strive towards not only achieving high accuracy of the generated recommendations but also protecting the user’s privacy and making recommender systems aware of the user’s context, which involves the user’s intentions and the user’s current situation. Through research it has been established that a tradeoff is required between the accuracy, the privacy and the risks in a recommender system and that it is highly unlikely to have recommender systems completely satisfying all the context-aware and privacy-preserving requirements. Nonetheless, a significant attempt can be made to describe a novel modeling approach that supports designing a recommender system encompassing some of these previously mentioned requirements. This thesis focuses on a multi-agent based system model of recommender systems by introducing both privacy and risk-related abstractions into traditional recommender systems and breaking down the system into three different subsystems. Such a description of the system will be able to represent a subset of recommender systems which can be classified as both risk-aware and privacy-preserving. The applicability of the approach is illustrated by a case study involving a job recommender system in which the general design model is instantiated to represent the required domain-specific abstractions

Security in Context-aware Mobile Business Applications

The support of location computation on mobile devices (e.g. mobile phones, PDAs) has enabled the development of context-aware and especially location-aware applications (e.g. Restaurant Finder, Friend Finder) which are becoming the new trend for future software applications. However, fears regarding security and privacy are the biggest barriers against their success. Especially, mobile users are afraid of the possible threats against their private identity and personal data. Within the M-Business research group at the University of Mannheim, various security and privacy aspects of context-aware mobile business applications are examined in this thesis. After providing a detailed introduction to context-aware applications, the security challenges of context-aware applications from the perspectives of different principals (i.e. mobile users, the broker, service providers) are analyzed. The privacy aspects, the challenges, the threats and legal directives regarding user privacy are explained and illustrated by real-life examples. The user-centric security architectures integrated within context-aware applications are introduced as anonymity and mobile identity management solutions. The M-Business security architecture providing security components for communication security, dynamic policy-based anonymity, secure storage on mobile devices, identity management for mobile users and cryptography libraries is explained in detail. The LaCoDa compiler which automatically generates final Java code from high level specifications of security protocols is introduced as a software-centric solution for preventing developer-specific security bugs in applications

Secret Consumer Scores and Segmentations: Separating “Haves” from “Have-Nots”

Article published in the Michigan State Law Review