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Secure semi-quantum summation entails the collective computation of the sum of private secrets by multi-untrustworthy and resource-limited participants, facilitated by a quantum third-party. This paper introduces three semi-quantum summation protocols based on single photons, where eliminating the need for classical users to possess measurement capabilities. Two-party protocol 1 and protocol 2 are structured upon different models: star and ring, respectively. The security analysis extensively evaluates the protocols’ resilience against outside and inside attacks, demonstrating protocols are asymptotically secure. Protocol 3 extends two-party protocol 1 to multi-party scenarios, broadening its applicability. Comparison reveals a reduction in the workload for classical users compared to previous similar protocols, and the protocols’ correctness are visually validated through simulation by Qiskit.

In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In order to solve the problem of inefficiency and high-complexity caused by traditional privacy preservation methods such as data encryption and access control technology, a privacy preservation method based on data coloring is proposed. The data coloring model is established and the coloring mechanism is adopted to deal with the sensitive data of numerical attributes, and the cloud model similarity measurement based on arithmetic average least-approximability is adopted to authenticate the ownership of privacy data. On the premise of high availability of data, the method strengthens the security of the privacy information. Then, the performance, validity and the parameter errors of the algorithm are quantitatively analyzed by the experiments using the UCI dataset. Under the same conditions of privacy preservation requirements, the proposed method can track privacy leakage efficiently and reduce privacy leakage risks. Compared with the k-anonymity approach, the proposed method enhances the computational time efficiency by 18.5%.