With the rapid development of information technology and intelligence, there are more and more usage scenarios of graph data. Path queries have always been a hot topic of research for scholars. There are already many mature methods and ideas in the study of the path query on the plaintext graph. For the path query on graph data in the case of cloud outsourcing, it is necessary to consider both the construction of query algorithms and the protection of privacy information. The processing of graph privacy information through encryption, and then outsourcing to the cloud platform, is a common measure. The semantic‐based path query supporting multikeyword search is an extended path query method, which can improve the query function. For users, it is very troublesome to access and process the encrypted graph data. In this article, we propose a protecting‐privacy semantic‐based multikeyword path query scheme on the ciphertext graph (PSMP). Firstly, based on the principle of searchable encryption and vector operation, a secure index is constructed, and then the cloud server uses the secure index to implement path queries. This article demonstrates its security through formal analysis and verifies its effectiveness through experimental comparison and analysis. The work of this article has a certain promoting effect on the query processing and analysis of ciphertext graph data.

An emerging dilemma that faces practitioners in large scale online experimentation for e-commerce is whether to use Multi-Armed Bandit (MAB) algorithms for testing or traditional A/B testing (A/B). This paper provides a comprehensive comparison between the two, from the perspectives of confidence intervals, hypothesis test powers, and their relationships with traffic split and sample size both theoretically and numerically. We first make comparisons between MAB with A/B tests in terms of conditions under which disjoint confidence intervals occur, and analyze their connection with the traffic split. Then we explore the relationship between the two in terms of sample sizes needed to achieve the required hypothesis test power, and analyze under what conditions MAB could have a higher test power than A/B given the same sample size. Based on the theoretical analysis, we propose two new MAB algorithms that combine the strengths of traditional MAB and A/B together, with higher (or equal) test power and higher (or equal) expected rewards than A/B testing under certain common conditions in e-commerce. Last, we evaluate and compare the performance among the classical MAB algorithms, our newly proposed MAB algorithms, and A/B testing in terms of their accuracy of identifying ground truth winner with practical significance, power rewards trade-off, sample sizes etc. in both simulated datasets and industrial historical datasets. We hope the work can not only facilitate a better understanding of pros and cons of MAB and A/B testing, but also help build the connections between the two and provide possible approaches that can leverage the best from both worlds.