Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses.

翻译：巨大图结构在组织与研究领域具有独特特性，例如社交网络中的用户关联性以及社交渠道中的客户评价矩阵。由于这些图结构规模庞大且持续扩展，需要大量资金进行维护。鉴于开放云资源在扩展存储空间与计算灵活性方面的广泛应用，大型图结构的所有者可能需要借助云资源。然而，云端的问责性与图结构保护已成为关键问题。本研究针对云服务器中图结构外包的核心分析实践——图谱异常分析，设计了安全高效的运算方案。我们构建了两种特征衰减计算的安全保护变体，分别采用两种密码学算法：加法同态加密（ASHE）策略与部分同态加密（SDHE）方法。此外，稀疏网络还引入了一种特殊隐私信息自适应协议，以平衡保密性与数据稀疏性。同时研究了稠密与稀疏两种结构。实验结果表明，采用稀疏编码的运算可大幅降低信息量。基于SDHE的方法计算时间较短，而基于ASHE的方法存储开销更低。