The proliferation of smart devices has advanced the application of face identification. Since face images involve user privacy, privacy-preserving face identification has attracted significant attention. the main goals of privacy-preserving face identification are i) achieving privacy against the participants and tasks; ii) achieving better identification performance and running efficiency. The existing homomorphic encryption-based protocols either suffer from large communication overheads or need many homomorphic rotations or homomorphic multiplication during the identification process. Motivated by the deficiencies of these existing approaches, we propose an efficient privacy-preserving face identification protocol that not only has lower communication costs but also achieves better efficiency. To this, a more compact and efficient algorithm for homomorphic computation of Euclidean distances is proposed. This homomorphic computation algorithm can homomorphicaly compute two Euclidean distances by only proceeding one homomorphic multiplication. Compared with the previous techniques, the proposed method saves about half of the space cost comparing with the single vector embedding methods and is at least 2× faster than the previous works. In addition, the method is independent of the proposed face identification protocol and thus can improve other biometric identification protocols straightforwardly. The experimental evaluations demonstrate that the proposed protocol not only performs almost the same identification rate as a plain protocol that provides no privacy but is also very efficient in adapting to the resource-limited small smart devices.

The proliferation of smart devices has propelled the advancement of IoT-based spatial crowdsourcing. The issue of location privacy in task allocation for IoT-based spatial crowdsourcing has attracted significant attention. Therefore, the main goals of privacy-preserving spatial crowdsourcing (PriSC) are: 1) achieving better location privacy for both participants and tasks and 2) achieving better allocation performance, i.e., accuracy and average moving distance. The homomorphic encryption-based approaches can achieve these goals, yet they suffer from heavy computation and large communication overhead. Although the differential privacy (DP)-based approaches are very efficient, these approaches leverage allocation performance to achieve better location privacy. Motivated by the deficiencies of these existing approaches, we propose a lightweight hash-based spatial crowdsourcing protocol, which not only protects both task location and participant location from the server but also reduces service providers’ computation and communication overhead. Besides, our design is independent of the concrete hash function and thus can be instantiated by any collision-resistant cryptographic hash function. Experiment results demonstrate that our protocol outperforms related works in terms of accuracy and average moving distance.

Permissioned blockchains play a significant role in various application scenarios. Applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, namedDataFly. We design apeg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) theintegrated signature and public key encryptionscheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality ofDataFly, we implement a prototype ofDataFlyusing existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate thatDataFlyoutperforms related works in terms of transaction latency and gas costs.

With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.