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Two-party ECDSA signatures have received much attention due to their widespread deployment in cryptocurrencies. Depending on whether or not the message is required, we could divide two-party signing into two different phases, namely, offline and online. Ideally, the online phase should be made as lightweight as possible. At the same time, the cost of the offline phase should remain similar to that of a normal signature generation. However, the existing two-party protocols of ECDSA are not optimal: either their online phase requires decryption of a ciphertext, or their offline phase needs at least two executions of multiplicative-to-additive conversion which dominates the overall complexity. This paper proposes an online-friendly two-party ECDSA with a lightweight online phase and a single multiplicative-to-additive function in the offline phase. It is constructed by a novel design of a re-sharing of the secret key and a linear sharing of the nonce. Our scheme significantly improves previous protocols based on either oblivious transfer or homomorphic encryption. We implement our scheme and show that it outperforms prior online-friendly schemes (i.e., those have lightweight online cost) by a factor of roughly 2 to 9 in both communication and computation. Furthermore, our two-party scheme could be easily extended to the 2-out-of-n threshold ECDSA.

There are two popular location-based service (LBS) applications: searching k-nearest neighbor Points of Interests (kNN POIs) and finding Nearby Friends (NF) via a social network server (SNS). Nevertheless, both applications are based on users’ current locations, and no scheme has been devised yet to merge POIs, NF and SNS together. A series of works were proposed to preserve users’ query privacy leaked from service attributes of POIs or location privacy over Mobile Social Networks (MSNs). However, their communication and computation costs are heavy.

Threshold ECDSA signatures provide a higher level of security to a crypto wallet since it requires more than t parties out of n parties to sign a transaction. The state-of-the-art bandwidth efficient threshold ECDSA used the additive homomorphic Castagnos and Laguillaumie (CL) encryption based on an unknown order group G, together with a number of zero-knowledge proofs in G. In this paper, we propose compact zero-knowledge proofs for threshold ECDSA to lower the communication bandwidth, as well as the computation cost. The proposed zero-knowledge proofs include the discrete-logarithm relation in G and the well-formedness of a CL ciphertext.

Docker container technology is an emerging virtualization technology which has a very high efficiency in the phases of development and deployment. Although Docker container technology shows better convenience than traditional virtualization technology—virtual machine, it suffers the poor security due to the unmatured auditing procedures of Docker image releasing. Thus, to protect the security of host computer or local Docker containers from the attacks of malicious Docker containers, it is necessary to detect the potential threats existing in Docker images and find the risks when Docker container instances run in the host computer. This paper gives a detailed analysis on Docker’s existing security mechanisms and the main threats Docker users must face. Finally, the corresponding threats detection techniques for Docker images and Docker container instances are presented, and the experiment result proves the effectivity of the proposed detection framework.

Webshell is a type of web backdoor which can be used by hackers to control web servers remotely. It is true that webshell becomes increasingly hard to detect because of the use of more and more hiding technologies, such decoding and encrypting. However, webshell still can be detected with high accuracy by virtue of machine learning algorithms. In this paper, we proposed a PHP webshell detecting model, the RF-GBDT (Random Forest–Gradient Boosting Decision Tree) model, which is the combination of random forest classifier and GBDT classifier. Besides, we not only used the common statistical features of PHP source files, such as information entropy, index of coincidence and so forth, but also extracted opcode sequence features from PHP source files, including TF-IDF vector and hash vector. Based on the RF-GBDT model and those effective features, the RF-GBDT PHP webshell prediction model shows an excellent performance, achieving the accuracy of 99.169% with false positive rate of 0.682%, shadowing several popular webshell detectors.