REPUTATION MANAGEMENT ALGORITHMS IN DISTRIBUTED APPLICATIONS

Nowadays, several distributed systems and applications rely on interactions between unknown agents that cooperate in order to exchange resources and services. The distributed nature of these systems, and the consequent lack of a single centralized point of control, let agents to adopt selfish and malicious behaviors in order to maximize their own utility. To address such issue, many applications rely on Reputation Management Systems (RMSs) to estimate the future behavior of unknown agents before establishing actual interactions. The relevance of these systems is even greater if the malicious or selfish behavior exhibited by a few agents may reduce the utility perceived by cooperative agents, leading to a damage to the whole community. RMSs allow to estimate the expected outcome of a given interaction, thus providing relevant information that can be exploited to take decisions about the convenience of interacting with a certain agent. Agents and their behavior are constantly evolving and becoming even more complex, so it is increasingly difficult to successfully develop the RMS, able to resist the threats presented. A possible solution to this problem is the use of agent-based simulation software designed to support researchers in evaluating distributed reputation management systems since the design phase. This dissertation presents the design and the development of a distributed simulation platform based on HPC technologies called DRESS. This solution allows researchers to assess the performance of a generic reputation management system and provides a comprehensive assessment of its ability to withstand security attacks. In the scientific literature, a tool that allows the comparison of distinct RMS and different design choices through a set of defined metrics, also supporting large-scale simulations, is still missing. The effectiveness of the proposed approach is demonstrated by the application scenario of user energy sharing systems within smart-grids and by considering user preferences differently from other work. The platform has proved to be useful for the development of an energy sharing system among users, which with the aim of maximizing the amount of energy transferred has exploited the reputation of users once learned their preferences

BLIND: A privacy preserving truth discovery system for mobile crowdsensing

Nowadays, an increasing number of applications exploit users who act as intelligent sensors and can quickly provide high-level information. These users generate valuable data that, if mishandled, could potentially reveal sensitive information. Protecting user privacy is thus of paramount importance for crowdsensing systems. In this paper, we propose BLIND, an innovative open-source truth discovery system designed to improve the quality of information (QoI) through the use of privacy-preserving computation techniques in mobile crowdsensing scenarios. The uniqueness of BLIND lies in its ability to preserve user privacy by ensuring that none of the parties involved are able to identify the source of the information provided. The system uses homomorphic encryption to implement a novel privacy-preserving version of the well-known K-Means clustering algorithm, which directly groups encrypted user data. Outliers are then removed privately without revealing any useful information to the parties involved. We extensively evaluate the proposed system for both server-side and client-side scalability, as well as truth discovery accuracy, using a real-world dataset and a synthetic one, to test the system under challenging conditions. Comparisons with four state-of-the-art approaches show that BLIND optimizes QoI by effectively mitigating the impact of four different security attacks, with higher accuracy and lower communication overhead than its competitors. With the optimizations proposed in this paper, BLIND is up to three times faster than the baseline system, and the obtained Root Mean Squared Error (RMSE) values are up to 42% lower than other state-of-the-art approaches

Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: a DES instance

Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential attack to the Data Encryption Standard (DES) which, despite being one of the methods that has been most thoroughly analyzed, is still of great interest to the scientific community since its vulnerabilities may have implications on other ciphers

Your Friends Mention It. What About Visiting It? A Mobile Social-Based Sightseeing Application

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing technique

DRESS: A Distributed RMS Evaluation Simulation Software

Distributed environments consist of a huge number of entities that cooperate to achieve complex goals. When interactions occur between unknown parties, intelligent techniques for estimating agents’ reputations are required. Reputation Management Systems (RMSs) allow agents to perform such estimation in a cooperative way. In particular, distributed RMSs exploit feedbacks provided after each interaction to predict future behaviors of agents. Such systems, are sensitive to fake information injected by malicious users, thus, predicting their performance is a very challenging task. Although many existing works have addressed some challenges concerning the design and assessment of specific RMSs, there are no simulation environments that adopt a general approach that can be applied to different application scenarios. To overcome this lack, in this work we present DRESS, an agent-based simulation framework that aims to support researchers in the evaluation of distributed RMSs under different security attacks

A Simulation Software for the Evaluation of Vulnerabilities in Reputation Management Systems

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs

A Platform for the Evaluation of Distributed Reputation Algorithms

In distributed environments, where unknown entities cooperate to achieve complex goals, intelligent techniques for estimating agents' truthfulness are required. Distributed Reputation Management Systems (RMSs) allow to accomplish this task without the need for a central entity that may represent a bottleneck and a single point of failure. The design of a distributed RMS is a challenging task due to a multitude of factors that could impact on its performances. In order to support the researcher in evaluating the RMS robustness against security attacks since its beginning design phase, in this work we present a distributed simulation environment that allows to model both the agent's behaviors and the logic of the RMS itself. Moreover, in order to compare at simulation time the performance of the designed distributed RMS with a baseline obtained by an ideal RMS, we introduce an omniscient process called truth-holder which owns a global knowledge all involved entities. The effectiveness of our platform was proved by a set of experiments aimed at measuring the vulnerability of a RMS to a common set of security attacks

Secure e-Voting in Smart Communities

Nowadays, digital voting systems are growing in importance. This is an especially sensitive area, because elections can directly affect democratic life of many smart communities. The goal of digital voting systems is to exploit ICT technologies to improve the security and usability of traditional electoral systems. In this work we present a secure electronic voting system that guarantees the secrecy, anonymity, integrity, uniqueness and authenticity of votes, while offering a user-friendly experience to voters, putting them at ease through the use of technologies familiar to them. To ensure these fundamental security requirements, the system fully separates the registration and voting phases and does not collect information on users, making it impossible to determine the identity of whoever cast each vote. Only the electoral supervisor, during the tallying phase, can decipher the electronic ballot papers, which are also totally anonymous. We consider universities to be one of the most representative smart communities, and for this reason we used the case study of university elections held on our campus to test the system. The experiments carried out tested the system in increasingly challenging scenarios, and were carried out by volunteer students and university staff members

SESAMO: An integrated framework for gathering, managing and sharing environmental data

ICT systems are widely adopted for environmental management, but existing solutions address limited tasks and compose a plethora of heterogeneous tools, which impose a great additional effort on the operators. This work presents SESAMO, a novel framework to provide the operators with a unique tool for gathering, managing and merging environmental and territorial data. SESAMO uses WSNs for providing pervasive monitoring of environmental phenomena and exploits a multi-tier infrastructure in order to integrate data coming from heterogeneous information sources