iPDA: An Integrity-Protecting Private Data Aggregation Scheme for Wireless Sensor Networks

Data aggregation is an efficient mechanism widely used in wireless sensor networks (WSN) to collect statistics about data of interests. However, the shared-medium nature of communication makes the WSNs are vulnerable to eavesdropping and packet tampering/injection by adversaries. Hence, how to protect data privacy and data integrity are two major challenges for data aggregation in wireless sensor networks. In this paper, we present iPDA??????an integrity-protecting private data aggregation scheme. In iPDA, data privacy is achieved through data slicing and assembling technique; and data integrity is achieved through redundancy by constructing disjoint aggregation paths/trees to collect data of interests. In iPDA, the data integrity-protection and data privacy-preservation mechanisms work synergistically. We evaluate the iPDA scheme in terms of the efficacy of privacy preservation, communication overhead, and data aggregation accuracy, comparing with a typical data aggregation scheme--- TAG, where no integrity protection and privacy preservation is provided. Both theoretical analysis and simulation results show that iPDA achieves the design goals while still maintains the efficiency of data aggregation

Enhancing Scalability and Reliability in Semi-Decentralized Federated Learning With Blockchain: Trust Penalization and Asynchronous Functionality

The paper presents an innovative approach to address the challenges of scalability and reliability in Distributed Federated Learning by leveraging the integration of blockchain technology. The paper focuses on enhancing the trustworthiness of participating nodes through a trust penalization mechanism while also enabling asynchronous functionality for efficient and robust model updates. By combining Semi-Decentralized Federated Learning with Blockchain (SDFL-B), the proposed system aims to create a fair, secure and transparent environment for collaborative machine learning without compromising data privacy. The research presents a comprehensive system architecture, methodologies, experimental results, and discussions that demonstrate the advantages of this novel approach in fostering scalable and reliable SDFL-B systems.Comment: To appear in 2023 IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference (IEEE UEMCON

Functional encryption based approaches for practical privacy-preserving machine learning

Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical. In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient

Privacy-aware secured discrete framework in wireless sensor network

Rapid expansion of wireless sensor network-internet of things (WSN-IoT) in terms of application and technologies has led to wide research considering efficiency and security aspects. Considering the efficiency approach such as data aggregation along with consensus mechanism has been one of the efficient and secure approaches, however, privacy has been one of major concern and it remains an open issue due to low classification and high misclassification rate. This research work presents the privacy and reliable aware discrete (PRD-aggregation) framework to protect and secure the privacy of the node. It works by initializing the particular variable for each node and defining the threshold; further nodes update their state through the functions, and later consensus is developed among the sensor nodes, which further updates. The novelty of PRD is discretized transmission for efficiency and security. PRD-aggregation offers reliability through efficient termination criteria and avoidance of transmission failure. PRD-aggregation framework is evaluated considering the number of deceptive nodes for securing the node in the network. Furthermore, comparative analysis proves the marginal improvisation in terms of discussed parameter against the existing protocol

Security and privacy issues in some special-puropse networks

This thesis is about providing security and privacy to new emergent applications which are based on special-purpose networks. More precisely, we study different aspects regarding security and privacy issues related to sensor networks, mobile ad hoc networks, vehicular ad hoc networks and social networks.Sensor networks consist of resource-constrained wireless devices with sensor capabilities. This emerging technology has a wide variety of applications related to event surveillance like emergency response, habitat monitoring or defense-related networks.Ad hoc networks are suited for use in situations where deploying an infrastructure is not cost effective or is not possible for any other reason. When the nodes of an ad hoc network are small mobile devices (e.g. cell phones or PDAs), such a network is called mobile ad hoc network. One of many possible uses of MANETs is to provide crisis management services applications, such as in disaster recovery, where the entire communication infrastructure is destroyed and reestablishing communication quickly is crucial. Another useful situation for MANETs is a scenario without fixed communication systems where there is the need for any kind of collaborative computing. Such situation can occur in both business and military environments.When the mobile nodes of a MANET are embedded in cars, such a network is called Vehicular Ad hoc Network (VANET). This kind of networks can be very useful to increase the road traffic safety and they will be deployed for real use in the forthcoming years. As a proof of that, eight important European vehicle manufacturers have founded the CAR 2 CAR Communication Consortium. This non-profit organisation is dedicated to the objective of further increasing traffic safety and efficiency by means of inter-vehicle communications.Social networks differ from the special-purpose networks commented above in that they are not physical networks. Social networks are applications that work through classic networks. They can be defined as a community of web users where each user can publish and share information and services. Social networks have become an object of study both in computer and social sciences, with even dedicated journals and conferences.The special-purpose networks described above provide a wide range of new services and applications. Even though they are expected to improve the society in several ways, these innovative networks and their related applications bring also security and privacy issues that must be addressed.This thesis solves some security and privacy issues related to such new applications and services. More specifically, it focuses on:·Secure information transmission in many-to-one scenarios with resource-constrained devices such as sensor networks.·Secure and private information sharing in MANETs.·Secure and private information spread in VANETs.·Private resource access in social networks.Results presented in this thesis include four contributions published in ISI JCR journals (IEEE Transactions on Vehicular Technology, Computer Networks (2) and Computer Communications) and two contributions published in two international conferences (Lecture Notes in Computer Science).Esta tesis trata diversos problemas de seguridad y privacidad que surgen al implantar en escenarios reales novedosas aplicaciones basadas en nuevos y emergentes modelos de red. Estos nuevos modelos de red difieren significativamente de las redes de computadores clásicas y son catalogadas como redes de propósito especial. Específicamente, en este trabajo se estudian diferentes aspectos relacionados con la seguridad de la información y la privacidad de los usuarios en redes de sensores, redes ad hoc móviles (MANETs), redes ad hoc vehiculares (VANETs) y redes sociales.Las redes de sensores están formadas por dispositivos inalámbricos muy limitados a nivel de recursos (capacidad de computación y batería) que detectan eventos o condiciones del entorno donde se instalan. Esta tecnología tiene una amplia variedad de aplicaciones entre las que destacan la detección de emergencias o la creación de perímetros de seguridad. Una MANET esta formada por nodos móviles conectados entre ellos mediante conexiones inalámbricas y de forma auto-organizada. Este tipo de redes se constituye sin la ayuda de infraestructuras, por ello son especialmente útiles en situaciones donde implantar una infraestructura es inviable por ser su coste demasiado elevado o por cualquier otra razón. Una de las muchas aplicaciones de las MANETs es proporcionar servicio en situaciones críticas (por ejemplo desastres naturales) donde la infraestructura de comunicaciones ha sido destruida y proporcionar conectividad rápidamente es crucial. Otra aplicación directa aparece en escenarios sin sistemas de comunicación fijos donde existe la necesidad de realizar algún tipo de computación colaborativa entre diversas máquinas. Esta situación se da tanto en ámbitos empresariales como militares.Cuando los nodos móviles de una MANET se asocian a vehículos (coches, camiones.), dicha red se denomina red ad hoc vehicular o VANET. Este tipo de redes pueden ser muy útiles para incrementar la seguridad vial y se espera su implantación para uso real en los próximos años. Como prueba de la gran importancia que tiene esta tecnología, los ocho fabricantes europeos más importantes han fundado la CAR 2 CAR Communication Consortium. Esta organización tiene como objetivo incrementar la seguridad y la eficiencia del tráfico mediante el uso de comunicaciones entre los vehículos.Las redes sociales se diferencian de las redes especiales descritas anteriormente en que éstas no son redes físicas. Las redes sociales son aplicaciones que funcionan a través de las redes de computadores clásicas. Una red de este tipo puede ser definida como una comunidad de usuarios web en donde dichos usuarios pueden publicar y compartir información y servicios. En la actualidad, las redes sociales han adquirido gran importancia ofreciendo un amplio abanico de posibilidades a sus usuarios: trabajar de forma colaborativa, compartir ficheros, búsqueda de nuevos amigos, etc.A continuación se resumen las aplicaciones en las que esta tesis se centra según el tipo de red asociada:·Transmisión segura de información en escenarios muchos-a-uno (múltiples emisores y un solo receptor) donde los dispositivos en uso poseen recursos muy limitados. Este escenario es el habitual en redes de sensores.·Distribución de información de forma segura y preservando la privacidad de los usuarios en redes ad hoc móviles.·Difusión de información (con el objeto de incrementar la seguridad vial) fidedigna preservando la privacidad de los usuarios en redes ad hoc vehiculares.·Acceso a recursos en redes sociales preservando la privacidad de los usuarios. Los resultados de la tesis incluyen cuatro publicaciones en revistas ISI JCR (IEEE Transactions on Vehicular Technology, Computer Networks (2) y Computer Communications) y dos publicaciones en congresos internacionales(Lecture Notes in Computer Science)

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table