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

Sharing knowledge without sharing data: on the false choice between the privacy and utility of information

Presentation slides for Azer Bestavros' June 1, 2017 talk at the BU Law School.As part of an ongoing collaboration, the Law School hosted a talk by Azer Bestavros, BU Professor of Computer Science and the Director of the Hariri Institute for Computing. Prof. Bestavros will detailed his groundbreaking research project regarding pay equity. In this project, he and his colleagues conducted a study of more than 170 employers in the Boston area, analyzing and reporting pay equity results without compromising any of the firms' confidentiality. The project - and the methodology - have broad implications well beyond the employment context

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

Economics and the Survivor Peasant

Peasants are survivor actors: they allocate all their resources and deploy refined strategies for securing a smooth horizon of consumption. Their stylized behavior is irrational only if development is the goal the peasant should follow. Subsistence as expression for describing rural economies is inadequate, since it doesn't connote the risk of starvation or death that peasants face. The survivor actor poses descriptive demands and normative implications. At a descriptive level, peasant's risk behavior is not ruled by inner preferences only, but depends on his expectations for securing a smooth consumption during the crop cycle. The utility model is apt for describing the survivor actor. Yet the exponent that defines the curvature of the utility includes a component that captures the aversion to uncertainty and a component that grasps the expectations about the chances to secure the horizon of consumption. This component defines a function of risk behavior, a counterpart of the Arrows-Pratt function of risk aversion. A normative for the survivor actor has to consider what is feasible, not what is desirable; what could be, not what should be. --