With the increasing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaption methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

We study persuasion mechanisms in linear environments. A receiver has a private type and chooses between two actions. A sender designs a persuasion mechanism or an experiment to disclose information about a payoff‐relevant state. A persuasion mechanism conditions information disclosure on the receiver's report about his type, whereas an experiment discloses information independent of the receiver's type. We establish the equivalence of implementation by persuasion mechanisms and by experiments, and characterize optimal persuasion mechanisms.

The COVID-19 pandemic has unexpectedly affected the educational process worldwide, forcing teachers and students to transfer to an online teaching and learning format. Compared with the traditional face-to-face teaching methods, teachers’ professional role, career satisfaction level, and digital literacy have been challenged in the COVID-19 health crisis. To conduct a systematic review, we use critical appraisal tools from the University of the West of England Framework We removed the irrelevant and lower-quality results to refine the results and scored each selected paper to get high-quality studies with STARLITE. The number of finally included studies is 21. We used the PICO mnemonic to structure the four components of a clinical question, i.e., the relevant patients or population groups, the intervention (exposure or diagnostic procedure) of interest, as well as against whom the intervention is being compared and considered appropriate (outcomes). We formulated five research questions regarding teachers’ professional role, satisfaction, digital literacy, higher educational practice, and sustainable education. The study found that teachers’ professional roles changed complicatedly. Moreover, they were assigned more tasks during the online teaching process, which also implicated a decline in teachers’ satisfaction. After the COVID-19 pandemic, it is necessary to conduct a blended teaching model in educational institutes. Teachers should have adequate digital literacy to meet the new needs of the currently innovative educational model in the future. In addition, the study reveals that teachers’ digital literacy level, career satisfaction, and professional role are significantly correlated. We measured to what degree the three factors affected the online teaching and learning process. Ultimately, the study may provide some suggestions for methodological and educational strategies.

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