Privacy Enhancing Techniques in the Internet of Things Using Data Anonymisation

The Internet of Things (IoT) and Industrial 4.0 bring enormous potential benefits by enabling highly customised services and applications, which create huge volume and variety of data. However, preserving the privacy in IoT and Industrial 4.0 against re-identification attacks is very challenging. In this work, we considered three main data types generated in IoT: context data, continuous data, and media data. We first proposed a stream data anonymisation method based on k-anonymity for data collected by IoT devices; and then privacy enhancing techniques for both continuous data and media data were proposed for different IoT scenarios. The experiment results show that the proposed techniques can well preserve privacy without significantly affecting the utility of the data.

Mix Networks: Existing Scenarios and Future Directions on Security and Privacy

Background: Privacy enhancing techniques are developed in order to provide strong protection to cyberspace. These techniques aim to allow users to keep their identities hidden during the communication when they are sending email, making payments online, browsing the Web or posting to newsgroups. MixNet is the most practical solution for concealing identities of message and sender’s identities. Objective: It provides sender and receiver anonymity as well as message security. The main goal of MixNet is to overcome vulnerability and enhance the performance of communication. It can efficiently handle the messages of various length and produce desirable results with privacy. The main objective of this paper is to acquire information and concepts regarding MixNet. We also provide guidelines for future research and references. Methods : The designing of MixNet depends on what cryptosystem method is used. Symmetric and Asymmetric both are used. Other methods could be also used such as PIR, CSP and FDR model, RPC, Token-based approach or others. Result: In this paper we provide an overview of MixNet approaches and presented a survey on MixNet based techniques and different models. We also constructed the comparison tables as per our requirements for better understanding. For this purpose, we found a total of 120 articles related to the MixNet published between 1990 and 2018 from the most relevant scientific resources. However, only 86 papers were analyzed due to comprehensiveness and relevancy to this article. Conclusion: Focused on the security and privacy of communication and how it can be achieved. This survey also reveals research progress and research gap on MixNet.

Enhancing Privacy and Improving Security in Scalable Blockchain

Bitcoin is a decentralized blockchain-based cryptocurrency that has taken the world by storm. Since its introduction in 2009, it has grown tremendously in terms of popularity and market cap. The idea of having a decentralized public ledger while maintaining anonymity and security attracted the attention of developers and customers alike. Special nodes in the bitcoin network, called miners, are responsible for making the network secure by using a concept called proof-of-work. A certain degree of anonymity is also maintained as no personally identifiable information of a person, like name, address, etc., is linked to the bitcoin wallet. In terms of bitcoin, a user is anonymous if different interactions of the user cannot be linked to each other or the user. Recent research shows that bitcoin is not as anonymous as it appears to be. The inherently public nature of blockchain technology makes it difficult to achieve privacy. The purpose of this paper is to review how varying degrees of user privacy is maintained in bitcoin cryptocurrency. This paper is divided into two main segments. The first segment explores privacy-enhancing techniques adopted in bitcoin. The second segment critically analyzes these techniques.