An Algorithm for Differential Privacy Streaming Data Adaptive Publication
-
摘要: 当前，许多实际应用需要持续地对流数据进行发布，现有关于单条流数据的差分隐私发布研究大多考虑区间的累和发布，而现实应用中往往需要对发布流数据进行任意区间计数查询，同时，用户查询往往存在特定规律，可针对历史查询进行自适应统计与分析，提高发布数据可用性.为此，提出一个基于历史查询的差分隐私流数据自适应发布算法HQ_DPSAP.算法HQ_DPSAP首先结合流数据的特性，利用滑动窗口机制动态构建窗口内流数据对应的差分隐私区间树，而后进一步分析与计算树节点的覆盖概率；接着自底向上计算隐私分配参数，再自顶向下分配隐私预算，并据此对树节点进行异方差加噪；最后根据历史查询规律自适应调整树节点的隐私预算与树结构参数，以实现流数据的自适应发布.实验对算法HQ_DPSAP的可行性及有效性进行比较分析，结果表明：算法HQ_DPSAP可有效支持任意区间计数查询，且具有较低的查询均方误差和较高的算法执行效率.Abstract: Nowadays, many practical applications need to publish streaming data continuously. Most of existing research works for differential privacy single streaming data publication focus on range accumulation. However, many practical scenarios need to answer arbitrary range counting queries of streaming data. At the same time, there exist specific rules of queries from users, so adaptive analysis and calculation for historical queries should be concerned. To improve the usability of published data, an algorithm HQ_DPASP for differential privacy streaming data adaptive publication based on historical queries is proposed. Combining the characteristics of streaming data, HQ_DPASP firstly uses the sliding window mechanism to construct the differential privacy range tree of the streaming data dynamically. Secondly, by analyzing the coverage probability of tree nodes and calculating the privacy parameters from leaves to root, HQ_DPASP allocates privacy budget from root to leaves and adds non-uniform noise on tree nodes. Finally, the privacy budget of tree nodes and tree's parameters are adjusted adaptively based on the characteristic of historical queries. Experiments are designed for testing the feasibility and effectiveness of HQ_DPSAP. The results show that HQ_DPSAP is effective in answering arbitrary range counting queries on the published streaming data while assuring low mean squared error of queries and high algorithm efficiency.
-
-
期刊类型引用(9)
1. 杨海龙，靳新华. 基于ECC复合加密的医院网络隐私信息安全保护方法. 自动化技术与应用. 2024(08): 140-143+166 . 
百度学术
2. 贾卉楠，王斌. 基于移动群智感知的隐私保护研究. 佳木斯大学学报(自然科学版). 2024(09): 16-18+69 . 百度学术
3. 杨小琴，朱玉全. 网络加密数据跨平台迁移自适应决策模型构建. 计算机仿真. 2023(01): 437-440+516 . 百度学术
4. 蒋沥泉，秦志光. 基于属性隐藏的高效去中心化的移动群智数据共享方案. 电子科技大学学报. 2023(06): 915-924 . 百度学术
5. 蔡波. 马尔可夫预测的移动群智感知网络日志信息收集. 西安工程大学学报. 2022(01): 115-120 . 百度学术
6. 佘晓萌 ，杜洋 ，马文静 ，殷赵霞 . 基于像素预测和块标记的图像密文可逆信息隐藏. 计算机研究与发展. 2022(09): 2089-2100 . 本站查看
7. 王磊，陈磊，张明儒，魏敏，李晋先. 面向数据库查询的非结构化数据融合存储系统. 电子设计工程. 2022(24): 148-152 . 百度学术
8. 李卓，宋子晖，沈鑫，陈昕. 边缘计算支持下的移动群智感知本地差分隐私保护机制. 计算机应用. 2021(09): 2678-2686 . 百度学术
9. 熊金波，毕仁万，田有亮，刘西蒙，马建峰. 移动群智感知安全与隐私:模型、进展与趋势. 计算机学报. 2021(09): 1949-1966 . 百度学术
其他类型引用(13)
计量
- 文章访问数: 1349
- HTML全文浏览量: 0
- PDF下载量: 548
- 被引次数: 22
下载:
