Crowdsensing applications utilize the pervasive smartphone users to collect large-scale sensing data efficiently. The quality of sensing data depends on the participation of highly skilled users. To motivate these skilled users to participate, they should receive enough rewards for compensating their resource consumption. Available incentive mechanisms mainly consider the truthfulness of the mechanism, but mostly ignore the issues of security and privacy caused by a “trustful” center. In this paper, we propose a privacy-preserving blockchain incentive mechanism in crowdsensing applications, in which a cryptocurrency built on blockchains is used as a secure incentive way. High quality contributors will get their payments that are recorded in transaction blocks. The miners will verify the transaction according to the sensing data assessment criteria published by the server. As the transaction information can disclose users’ privacy, a node cooperation verification approach is proposed to achieve $k$ -anonymity privacy protection. Through theoretical analysis and simulation experiments, we show the feasibility and security of our incentive mechanism.

A Blockchain Based Privacy-Preserving Incentive Mechanism in Crowdsensing Applications

In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users' privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users' peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.

Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms.

Nowadays, with the advance of Internet technology, social network is getting popular, which combines the virtual network and the real world. People employ this network to communicate with all social things of their interest, including shopping, making friends, sharing experiences of life, and so on. In social networks, the social data expands rapidly and the malicious users can easily get the social data. With the social information, they could conjecture the relationship among social network data via the systematical analysis tool. Hence, the personal privacy data in social network may be exposed to some unknown risks, and recently, these issues arising in such a network catch much attention. The protection of personal privacy social data becomes an important and urgent research in social networks. One of personal privacy social information is the relations between the individuals and their social groups, namely human relationships.

K-anonymity against neighborhood attacks in weighted social networks

The Digital economy is based on confidence in its trustworthiness. Blockchain distributed consensus provides a reliable and trustful network for financial and non-financial transactions. Blockchain-based electric vehicles (EVs) charging applications benefit blockchain features to provide automated and verifiable services for EV charging market. Requirements for feasible charging operation and privacy concerns are challenging issues with blockchain-based EV charging approaches. To provide a feasible charging ability and preserve EV owner's privacy, we introduce EVChain. The EVChain is a trustful and decentralized platform based on blockchain technology to share charging credits in the EV charging market. To share credits, the main blockchain in EVChain is connected to one or more subnetwork blockchains. We introduce an interconnection position to preserve EV owners' privacy with k-anonymity protection. We simulate and evaluate the privacy protection it provides, based on an example EV charging scenario.

EVChain: A Blockchain-based Credit Sharing in Electric Vehicles Charging

Privacy preserving in social networks has been raised serious concerns in recent years. One of the privacy disclosures is brought about through structural attacks by malicious users. In this paper, we consider the privacy preserving publication against sensitive edges identification attacks in social networks, which are expressed by bipartite graphs. Firstly, we present a positive one-way (c1, c2)-security algorithm, a negative one-way (c1, c2)-security algorithm, and a two-way (c1, c2)-security algorithm. The proposed algorithms are based on the k-security group theory and the sensitive edges identification attacks in social networks. Furthermore, a bipartite graph anonymous problem is formally defined to against sensitive edges identification attacks. Meanwhile, a clustering-based bipartite graph anonymous method, Clustering-based bipartite (c1, c2)-security algorithm (CBB(c1, c2)-security), is proposed. The experimental results show that under the equal conditions, our method not only produces less information loss than that of the existing method, but also effectively resists sensitive edges identification attacks and realize security release of bipartite graphs.

http://www.joics.com/publishedpapers/2013_10_18_6031_6040.pdf

Zhang Jianpei

Papers

(d, k)-Anonymity for Social Networks Publication against Neighborhood Attacks

Research on CBK(L,K)-Anonymity Algorithm

An Improved RSLK-Anonymity Algorithm for Privacy Protection of Data Stream

K+-Isomorphism: Privacy Preserving Publication against Structural Attacks in Social Networks

An Improved Strategy of Preventing Privacy Inference Attacks Based on K-Anonymity Data Set