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Most traditional public key cryptosystems are constructed upon algebraically rich structures, which makes their key pairs combinable, i.e., the combination of some private keys and their corresponding public keys could form a new key pair. Exploring such combinable property, this paper proposes a novel Identity-Based Encryption (IBE) scheme based on the Diffie-Hellman Integrated Encryption Scheme (DHIES) with quadratic key combination structure from bilinear maps. The new scheme has a number of advantages over other IBE schemes. First, it uses DHIES to fulfill encryption, thus naturally obtains the security against adaptive chosen ciphertext attack from DHIES. Second, it is interoperable with existing security systems based on DHIES. Third, compared to many pairing-based IBE schemes, it only requires pairing computation during public key generation and there is no need for special hash function. We prove that our scheme is selective identity chosen ciphertext secure in the random oracle model assuming DHIES is chosen ciphertext secure. Additionally, the extract algorithm of our scheme also implies an identity-based short signature scheme.

While Ethereum smart contracts enabled a wide range of blockchain applications, they are extremely vulnerable to different forms of security attacks. Due to the fact that transactions to smart contracts commonly involve cryptocurrency transfer, any successful attacks can lead to money loss or even financial disorder. In this paper, we focus on the overflow attacks in Ethereum , mainly because they widely rooted in many smart contracts and comparatively easy to exploit. We have developed EASYFLOW , an overflow detector at Ethereum Virtual Machine level. The key insight behind EASYFLOW is a taint analysis based tracking technique to analyze the propagation of involved taints. Specifically, EASYFLOW can not only divide smart contracts into safe contracts, manifested overflows, well-protected overflows and potential overflows, but also automatically generate transactions to trigger potential overflows. In our preliminary evaluation, EASYFLOW managed to find potentially vulnerable Ethereum contracts with little runtime overhead. A demo video of EASYFLOW is at https://youtu.be/QbUJkQI0L6o.

False data filtering schemes are designed to filter out false data injected by malicious sensors; they keep the network immune to bogus event reports. Theoretic understanding of false data filtering schemes and guidelines to further improve their designs are still lacking. This article first presents an information-theoretic model of false data filtering schemes. From the information-theoretic view, we define the scheme's filtering capacity C F i as the uncertainty-reduction ratio of the target input variable, given the output. This metric not only performs better than existing metrics but also implies that only by optimizing the false negative rate and false positive rate simultaneously, can we promote a scheme's overall performance. Based on the investigation from the modeling efforts, we propose HiFi , a hybrid authentication-based false data filtering scheme. HiFi leverages the benefits of both symmetric and asymmetric cryptography and achieves a high filtering capacity, as well as low computation and communication overhead. Performance analysis demonstrates that our proposed metric is rational and useful, and that HiFi is effective and energy efficient.

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