Privacy in targeted advertising on mobile devices: a survey.

Targeted advertising has transformed the marketing landscape for a wide variety of businesses, by creating new opportunities for advertisers to reach prospective customers by delivering personalised ads, using an infrastructure of a number of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process, and trade a vast amount of users' personal data, which has prompted serious privacy concerns among both individuals and organisations. This article presents a comprehensive survey of the privacy risks and proposed solutions for targeted advertising in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, the measurement analysis of targeted advertising based on user's interests and profiling context, and the ads delivery process, for both in-app and in-browser targeted ads; we also include an overview of data sharing and tracking technologies. We discuss challenges in preserving the mobile user's privacy that include threats related to private information extraction and exchange among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks. Subsequently, we present various techniques for preserving user privacy and a comprehensive analysis of the proposals based on such techniques; we compare the proposals based on the underlying architectures, privacy mechanisms, and deployment scenarios. Finally, we discuss the potential research challenges and open research issues.

A Decentralized Architecture for Trusted Dataset Sharing Using Smart Contracts and Distributed Storage.

The data economy is based on data and information sharing and tremendously impacts society as it facilitates innovative collaborations and decision-making strategies. Nonetheless, most dataset-sharing solutions rely on a centralized authority that rules data ownership, availability, and accessibility. Recent works have explored the integration of distributed storage and blockchain to enhance decentralization, data access, and smart contracts for automating the interactions between actors and data. However, current solutions propose a smart contract design limiting the system's scalability in terms of actors and shared datasets. Furthermore, little is known about the performance of these architectures when using distributed storage instead of centralized storage approaches. This paper proposes a scalable architecture called DeBlock for data sharing in a trusted way among unreliable actors. The architecture integrates a public blockchain that provides a transparent record of datasets and interactions, with a distributed storage for data storage in a completely decentralized way. Furthermore, the architecture provides a smart-contract design for a transparent catalog of datasets, actors, and interactions with efficient search and retrieval capabilities. To assess the system's feasibility, robustness, and scalability, we implement a prototype using the Ethereum blockchain and leveraging two decentralized storage protocols, Swarm and IPFS. We evaluate the performance of our proposed system in different scenarios (e.g., varying the amount and size of the shared datasets). Our results demonstrate that our proposal outperforms benchmarks in gas consumption, latency, and resource requirements, especially when increasing the number of actors and shared datasets.

DIMY: Enabling privacy-preserving contact tracing.

The infection rate of COVID-19 and the rapid mutation ability of the virus has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the virus's spread. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although several digital contact tracing apps are proposed and deployed, these have not been widely adopted due to apprehensions surrounding privacy and security. In this paper, we present a blockchain-based privacy-preserving contact tracing protocol,"Did I Meet You" (DIMY). The protocol provides full-lifecycle data privacy protection on the devices as well as the back-end servers to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage and have used the Diffie-Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well-known attacks while introducing negligible overheads. DIMY's footprint on the storage space of clients' devices and back-end servers is also significantly lower than other similar state-of-the-art apps.

A machine learning and blockchain based secure and cost-effective framework for minor medical consultations.

With the ever-increasing awareness among people regarding their health, visiting a doctor has become quite common. However, with the onset of the COVID-19 pandemic, home-based consultations are gaining popularity. Nevertheless, the worries over privacy and the lack of willingness to assist patients by the medical professionals in the online consultation process have made current models ineffective. In this paper, we present an advanced protected blockchain-based consultation model for minor medical conditions. Our model not only ensures users' privacy but by incorporating a calculation model, it also offers an opportunity for consulting end-users to voluntarily take part in the consultation process. Our work proposes a smart contract based on machine learning to be implemented for the prediction of a score of a professional who consults based on various prioritized parameters. This is done by using word2vec and TF-IDF weighting to classify the question and cosine similarity scores for detailed orientation analysis. Based on this score, the patient is charged, and simultaneously, the responder is awarded ether. An incentivized method leads to more accessible healthcare while reducing the cost itself.

Efficient Data Communication Using Distributed Ledger Technology and IOTA-Enabled Internet of Things for a Future Machine-to-Machine Economy.

A potential rise in interest in the Internet of Things in the upcoming years is expected in the fields of healthcare, supply chain, logistics, industries, smart cities, smart homes, cyber physical systems, etc. This paper discloses the fusion of the Internet of Things (IoT) with the so-called "distributed ledger technology" (DLT). IoT sensors like temperature sensors, motion sensors, GPS or connected devices convey the activity of the environment. Sensor information acquired by such IoT devices are then stored in a blockchain. Data on a blockchain remains immutable however its scalability still remains a challenging issue and thus represents a hindrance for its mass adoption in the IoT. Here a communication system based on IOTA and DLT is discussed with a systematic architecture for IoT devices and a future machine-to-machine (M2M) economy. The data communication between IoT devices is analyzed using multiple use cases such as sending DHT-11 sensor data to the IOTA tangle. The value communication is analyzed using a novel "micro-payment enabled over the top" (MP-OTT) streaming platform that is based on the "pay-as-you-go" and "consumption based" models to showcase IOTA value transactions. In this paper, we propose an enhancement to the classical "masked authenticated message" (MAM) communication protocol and two architectures called dual signature masked authenticated message (DSMAM) and index-based address value transaction (IBAVT). Further, we provided an empirical analysis and discussion of the proposed techniques. The implemented solution provides better address management with secured sharing and communication of IoT data, complete access control over the ownership of data and high scalability in terms of number of transactions that can be handled.

A Decentralized Peer-to-Peer Remote Health Monitoring System.

Within the Internet of Things (IoT) and blockchain research, there is a growing interest in decentralizing health monitoring systems, to provide improved privacy to patients, without relying on trusted third parties for handling patients' sensitive health data. With public blockchain deployments being severely limited in their scalability, and inherently having latency in transaction processing, there is room for researching and developing new techniques to leverage the security features of blockchains within healthcare applications. This paper presents a solution for patients to share their biomedical data with their doctors without their data being handled by trusted third party entities. The solution is built on the Ethereum blockchain as a medium for negotiating and record-keeping, along with Tor for delivering data from patients to doctors. To highlight the applicability of the solution in various health monitoring scenarios, we have considered three use-cases, namely cardiac monitoring, sleep apnoea testing, and EEG following epileptic seizures. Following the discussion about the use cases, the paper outlines a security analysis performed on the proposed solution, based on multiple attack scenarios. Finally, the paper presents and discusses a performance evaluation in terms of data delivery time in comparison to existing centralized and decentralized solutions.

Eavesdropping and Jamming Selection Policy for Suspicious UAVs Based on Low Power Consumption over Fading Channels.

Traditional wireless security focuses on preventing unmanned aerial vehicle (UAV) communications from suspicious eavesdropping and/or jamming attacks. However, there is a growing need for governments to keep malicious UAV communications under legitimate surveillance. This paper first investigates a new surveillance paradigm for monitoring suspicious UAV communications via jamming suspicious UAVs. Due to the power consumption limitation, the choice of eavesdropping and jamming will reflect the performance of the UAVs communication. Therefore, the paper analyses the UAV's eavesdropping and jamming models in different cases, and then proposes the model to optimize the data package in the constraints of lower power consumption, which can be solved by the proposed selection policy. The simulation results validate our proposed selection policy in terms of power consumption and eavesdropped packets. In different fading models, power consumption increases with time, regardless of distances, and our proposed policy performs better in Weibull fading channels in terms of eavesdropped packets.