User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

A Human-Centric Digital Manifesto for Europe: How the Digital Transformation Can Serve the Public Interest

The digital revolution is radically changing every aspect of human life in the 21st century and it's essential that regulators address the challenges this transformation brings. The new European Commission should look beyond digital single market issues, and focus on creating new rights-based policies and regulations based on freedom, democracy, equality, and rule of law to ensure that existing offline rights are protected online

Security for the Industrial IoT: The Case for Information-Centric Networking

Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner. In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.Comment: To be published at IEEE WF-IoT 201

POVERTY LAWGORITHMS A Poverty Lawyer’s Guide to Fighting Automated Decision-Making Harms on Low-Income Communities

Automated decision-making systems make decisions about our lives, and those with low-socioeconomic status often bear the brunt of the harms these systems cause. Poverty Lawgorithms: A Poverty Lawyers Guide to Fighting Automated Decision-Making Harms on Low-Income Communities is a guide by Data & Society Faculty Fellow Michele Gilman to familiarize fellow poverty and civil legal services lawyers with the ins and outs of data-centric and automated-decision making systems, so that they can clearly understand the sources of the problems their clients are facing and effectively advocate on their behalf