ID 原文 译文
45656 基于真实的新浪微博数据集和知乎数据集，通过一系列对比实验证明，跨平台用户推荐模型可以更加全面准确地刻画用户行为，更好地进行用户推荐。 Based on the proposed model and the experimental results, it can be known that modeling users incross-platform online social networks can describe the user more comprehensively and leads to a better recommendation.
45657 基于层次细化的差分隐私决策数据发布得到了研究者的广泛关注，层次节点的选择、分类树的构建以及每层隐私代价的分配直接制约着决策数据发布结果的好坏，也影响最终的数据分析结果。 Specialization-based private decision data release has attracted considerable research attention in recent years. The relation among hierarchical node, taxonomy tree, and budget allocation directly constrains the accuracy of data re-lease and classification.
45658 现有基于层次细化的决策数据发布方法难以兼顾上述问题的不足， Most existing methods based on hierarchical specialization cannot efficiently address the above problems.
45659 提出一种高效的分层细化方法 MAXGDDP，该方法对原始分类数据进行分层细化， An effective method was proposed, called MAXGDDP to publish decision data with specialization.
45660 在同一层次的概念细化中提出了最大值属性索引算法， MAXGDDP employed MAX index attribute selection algorithm to select the highlight concept for furthering specialization in each hierarchy.
45661 在不同层次之间利用类几何分配机制来更加合理地分配隐私预算。 Besides, for making more rational use of privacy budget, MAXGDDP relied on geometric strategy to allocate the privacy budget in each hierarchy.
45662 基于真实数据集对比了 MAXGDDP 与 DiffGen 算法，实验结果表明该方法在保护数据隐私的同时，提高了发布数据的分类准确率。 Compared with existing methods such as DiffGen on the real datasets,MAXGDDP outperforms its competitors, achieves data privacy and the better result of classification simultaneously.
45663 理想格上的加密方案具有密钥尺寸小、加密效率高的优势，利用理想格环上带误差学习（R-LWE, ring learning with error）问题，构造一种可以保护用户属性隐私的属性基加密方案，支持灵活的访问策略，提供用户隐私保护，并且提高方案效率，缩短密钥尺寸。 Based on the small key size and high encryption efficiency on ideal lattices, a privacy-preserving attribute-based encryption scheme on ideal lattices was proposed, which could support flexible access policies and privacy protection for the users.
45664 该方案通过采用半策略隐藏方式，保护用户的隐私， In the scheme, a semi-hidden policy was introduced to protect the users’ privacy.
45665 从而避免用户的敏感属性值泄露给其他任何第三方。 Thus, the sensitive values of user’s attributes are hidden to prevent from revealing to any third parties.