PrivHab : A privacy preserving georouting protocol based on a multiagent system for podcast distribution on disconnected areas

Altres ajuts: Universitat Autònoma de Barcelona 472-03-01/2012We present PrivHab, a privacy preserving georouting protocol that improves multiagent decision-making. PrivHab learns the mobility habits of the nodes of the network. Then, it uses this information to dynamically select to route an agent carrying a piece of data to reach its destination. PrivHab makes use of cryptographic techniques from secure multi-party computation to make the decisions while preserving nodes' privacy. PrivHab uses a waypoint-based routing that achieves a high performance and low overhead in rugged terrain areas that are plenty of physical obstacles. The store-carry-and-forward approach used is combined with mobile agents that provide intelligence, and it is designed to operate in areas that lack network infrastructure. We have evaluated PrivHab under the scope of a realistic podcast distribution application in remote rural areas, where these programs have to be recorded into a physical format and distributed to the local radio stations. The usage of PrivHab aims to reduce this spending of resources. The PrivHab protocol is compared with a set of well-known delay-tolerant routing algorithms and shown to outperform them

User-centric privacy preservation in Internet of Things Networks

Recent trends show how the Internet of Things (IoT) and its services are becoming more omnipresent and popular. The end-to-end IoT services that are extensively used include everything from neighborhood discovery to smart home security systems, wearable health monitors, and connected appliances and vehicles. IoT leverages different kinds of networks like Location-based social networks, Mobile edge systems, Digital Twin Networks, and many more to realize these services. Many of these services rely on a constant feed of user information. Depending on the network being used, how this data is processed can vary significantly. The key thing to note is that so much data is collected, and users have little to no control over how extensively their data is used and what information is being used. This causes many privacy concerns, especially for a na ̈ıve user who does not know the implications and consequences of severe privacy breaches. When designing privacy policies, we need to understand the different user data types used in these networks. This includes user profile information, information from their queries used to get services (communication privacy), and location information which is much needed in many on-the-go services. Based on the context of the application, and the service being provided, the user data at risk and the risks themselves vary. First, we dive deep into the networks and understand the different aspects of privacy for user data and the issues faced in each such aspect. We then propose different privacy policies for these networks and focus on two main aspects of designing privacy mechanisms: The quality of service the user expects and the private information from the user’s perspective. The novel contribution here is to focus on what the user thinks and needs instead of fixating on designing privacy policies that only satisfy the third-party applications’ requirement of quality of service

A Privacy Preserving Prediction-based Routing Protocol for Mobile Delay Tolerant Networks

Abstract—A prediction-based routing protocol for mobile delay tolerant networks functions by forwarding a message from one intermediate node to another if the latter has higher probability of encountering the destination node. However, this process compromises the privacy of the nodes by revealing their mobility patterns. In this paper, we propose a privacy preserving predictionbased routing protocol that forwards messages by comparing information about communities of nodes instead of individual nodes. Specifically, it compares the maximum probability that a node in the community of a potential intermediate node will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our protocol has comparable performance to existing prediction-based protocols. Yet our protocol is the only one that preserves the privacy of nodes. Index Terms—privacy, routing protocols, mobile computing, delay tolerant network

A Privacy Preserving Prediction-based Routing Protocol for Mobile Delay Tolerant Networks

International audienceA prediction-based routing protocol for mobile delay tolerant networks functions by forwarding a message from one intermediate node to another if the latter has higher probability of encountering the destination node. However, this process compromises the privacy of the nodes by revealing their mobility patterns. In this paper, we propose a privacy preserving prediction-based routing protocol that forwards messages by comparing information about communities of nodes instead of individual nodes. Specifically, it compares the maximum probability that a node in the community of a potential intermediate node will encounter the destination node. We present theoretical security analyses as well as practical performance evaluations. Our simulations on a well established community-based mobility model demonstrate that our protocol has comparable performance to existing prediction-based protocols. Yet our protocol is the only one that preserves the privacy of nodes