Accelerating Scientific Discovery by Formulating Grand Scientific Challenges

One important question for science and society is how to best promote scientific progress. Inspired by the great success of Hilbert's famous set of problems, the FuturICT project tries to stimulate and focus the efforts of many scientists by formulating Grand Challenges, i.e. a set of fundamental, relevant and hardly solvable scientific questions.Comment: To appear in EPJ Special Topics. For related work see http://www.futurict.eu and http://www.soms.ethz.c

Decentralized Collaborative Learning Framework for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy. We evaluate DCLR with two real-world datasets, where the results show that DCLR outperforms state-of-the-art on-device frameworks and yields competitive results compared with centralized counterparts.Comment: 21 Pages, 3 figures, 4 table

Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Robust Trust Establishment in Decentralized Networks

The advancement in networking technologies creates new opportunities for computer users to communicate and interact with one another. Very often, these interacting parties are strangers. A relevant concern for a user is whether to trust the other party in an interaction, especially if there are risks associated with the interaction. Reputation systems are proposed as a method to establish trust among strangers. In a reputation system, a user who exhibits good behavior continuously can build a good reputation. On the other hand, a user who exhibits malicious behavior will have a poor reputation. Trust can then be established based on the reputation ratings of a user. While many research efforts have demonstrated the effectiveness of reputation systems in various situations, the security of reputation systems is not well understood within the research community. In the context of trust establishment, the goal of an adversary is to gain trust. An adversary can appear to be trustworthy within a reputation system if the adversary has a good reputation. Unfortunately, there are plenty of methods that an adversary can use to achieve a good reputation. To make things worse, there may be ways for an attacker to gain an advantage that may not be known yet. As a result, understanding an adversary is a challenging problem. The difficulty of this problem can be witnessed by how researchers attempt to prove the security of their reputation systems. Most prove security by using simulations to demonstrate that their solutions are resilient to specific attacks. Unfortunately, they do not justify their choices of the attack scenarios, and more importantly, they do not demonstrate that their choices are sufficient to claim that their solutions are secure. In this dissertation, I focus on addressing the security of reputation systems in a decentralized Peer-to-Peer (P2P) network. To understand the problem, I define an abstract model for trust establishment. The model consists of several layers. Each layer corresponds to a component of trust establishment. This model serves as a common point of reference for defining security. The model can also be used as a framework for designing and implementing trust establishment methods. The modular design of the model can also allow existing methods to inter-operate. To address the security issues, I first provide the definition of security for trust establishment. Security is defined as a measure of robustness. Using this definition, I provide analytical techniques for examining the robustness of trust establishment methods. In particular, I show that in general, most reputation systems are not robust. The analytical results lead to a better understanding of the capabilities of the adversaries. Based on this understanding, I design a solution that improves the robustness of reputation systems by using accountability. The purpose of accountability is to encourage peers to behave responsibly as well as to provide disincentive for malicious behavior. The effectiveness of the solution is validated by using simulations. While simulations are commonly used by other research efforts to validate their trust establishment methods, their choices of simulation scenarios seem to be chosen in an ad hoc manner. In fact, many of these works do not justify their choices of simulation scenarios, and neither do they show that their choices are adequate. In this dissertation, the simulation scenarios are chosen based on the capabilities of the adversaries. The simulation results show that under certain conditions, accountability can improve the robustness of reputation systems

Computational Challenges in E-Commerce

Economic and social sciences will drive Internet protocols and services into the future.Engineering and Applied Science

Accelerating scientific discovery by formulating grand scientific challenges

One important question for science and society is how to best promote scientific progress. Inspired by the great success of Hilbert's famous set of problems, the FuturICT project tries to stimulate and focus the efforts of many scientists by formulating Grand Challenges, i.e. a set of fundamental, relevant and hardly solvable scientific questions. Graphical abstrac