A Survey on Privacy Preserving Data Aggregation Protocols forWireless Sensor Networks

The data aggregation is a widely used mechanism in Wireless Sensor Networks (WSNs) to increase lifetime of a sensor node, send robust information by avoiding redundant data transmission to the base station. The privacy preserving data aggregation is a challenge in wireless communication medium as it could be eavesdropped; however it enhances the security without compromising energy efficiency. Thus the privacy protecting data aggregation protocols aims to prevent the disclosure of individual data though an adversary intercept a link or compromise a node’s data. We present a study of different privacy preserving data aggregation techniques used in WSNs to enhance energy and security based on the types of nodes in the network, topology and encryptions used for data aggregation.</p

Security and Privacy in Heterogeneous Wireless and Mobile Networks: Challenges and Solutions

abstract: The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.Dissertation/ThesisPh.D. Electrical Engineering 201

Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks: A Survey

Many wireless sensor network (WSN) applications require privacy-preserving aggregation of sensor data during transmission from the source nodes to the sink node. In this paper, we explore several existing privacy-preserving data aggregation (PPDA) protocols for WSNs in order to provide some insights on their current status. For this, we evaluate the PPDA protocols on the basis of such metrics as communication and computation costs in order to demonstrate their potential for supporting privacy-preserving data aggregation in WSNs. In addition, based on the existing research, we enumerate some important future research directions in the field of privacy-preserving data aggregation for WSNs

Efficient and Error-bounded Spatiotemporal Quantile Monitoring in Edge Computing Environments

Underlying many types of data analytics, a spatiotemporal quantile monitoring (SQM) query continuously returns the quantiles of a dataset observed in a spatiotemporal range. In this paper, we study SQM in an Internet of Things (IoT) based edge computing environment, where concurrent SQM queries share the same infrastructure asynchronously. To minimize query latency while providing result accuracy guarantees, we design a processing framework that virtualizes edge-resident data sketches for quantile computing. In the framework, a coordinator edge node manages edge sketches and synchronizes edge sketch processing and query executions. The co-ordinator also controls the processed data fractions of edge sketches, which helps to achieve the optimal latency with error-bounded results for each single query. To support concurrent queries, we employ a grid to decompose queries into subqueries and process them efficiently using shared edge sketches. We also devise a relaxation algorithm to converge to optimal latencies for those subqueries whose result errors are still bounded. We evaluate our proposals using two high-speed streaming datasets in a simulated IoT setting with edge nodes. The results show that our proposals achieve efficient, scalable, and error-bounded SQM

Verifiable private multi-party computation: Ranging and ranking

Abstract—The existing work on distributed secure multi-party computation, e.g., set operations, dot product, ranking, focus on the privacy protection aspects, while the verifiability of user inputs and outcomes are neglected. Most of the existing works assume that the involved parties will follow the protocol honestly. In practice, a malicious adversary can easily forge his/her input values to achieve incorrect outcomes or simply lie about the computation results to cheat other parities. In this work, we focus on the problem of verifiable privacy preserving multi-party computation. We thoroughly analyze the attacks on existing privacy preserving multi-party computation approaches and design a series of protocols for dot product, ranging and ranking, which are proved to be privacy preserving and verifiable. We implement our protocols on laptops and mobile phones. The results show that our verifiable private computation protocols are efficient both in computation and communication

Architecting a Blockchain-Based Framework for the Internet of Things

Traditionally, Internet-of-Things (IoT) solutions are based on centralized infrastructures, which necessitate high-end servers for handling and transferring data. Centralized solutions incur high costs associated to maintaining centralized servers, and do not provide built-in guarantees against security threats and trust issues. Therefore, it is an essential research problem to mitigate the aforementioned problems by developing new methods for IoT decentralisation. In recent years, blockchain technology, the underlying technology of Bitcoin, has attracted research interest as the potential missing link towards building a truly decentralized, trustless and secure environment for the IoT. Nevertheless, employing blockchain in the IoT has significant issues and challenges, related to scalability since all transactions logged in a blockchain undergo a decentralized consensus process. This thesis presents the design and implementation of a blockchain-based decentralized IoT framework that can leverage the inherent security characteristics of blockchains, while addressing the challenges associated with developing such a framework. This decentralized IoT framework aims to employ blockchains in combination with other peer-to-peer mechanisms to provide: access control; secure IoT data transfer; peer-to-peer data-sharing business models; and secure end-to-end IoT communications, without depending upon a centralized intermediary for authentication or data handling. This framework uses a multi-tiered blockchain architecture with a control-plane/data-plane split, in that the bulk data is transferred through peer-to-peer data transfer mechanisms, and blockchains are used to enforce terms and conditions and store relevant timestamped metadata. Implementations of the blockchain-based framework have been presented in a multitude of use-cases, to observe the framework's viability and adaptability in real-world scenarios. These scenarios involved traceability in supply chains, IoT data monetization and security in end-to-end communications.With all the potential applications of the blockchain-based framework within the IoT, this thesis takes a step towards the goal of a truly decentralized IoT

Sheltered Compound Vendor Data Apportioning For Vibrant Clusters In The Cloud

Cloud computing is an emerging computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. Sharing data in a multiowner manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due to the frequent change of the membership. To preserve data privacy, a basic solution is to encrypt data files, and then upload the encrypted data into the cloud. In this paper we are further extending the basic MONA by adding the reliability as well as improving the scalability by increasing the number of group managers dynamically. This paper proposes how user can access data after the time out. The storage overhead and encryption computation cost of our scheme are independent with the number of revoked users