Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3114-3121.DOI: 10.11772/j.issn.1001-9081.2023101520
• Cyber security • Previous Articles Next Articles
Xuebin CHEN1,2,3(), Liyang SHAN1,2,3, Rumin GUO1,2,3
Received:
2023-11-07
Revised:
2024-01-02
Accepted:
2024-01-04
Online:
2024-01-19
Published:
2024-10-10
Contact:
Xuebin CHEN
About author:
SHAN Liyang, born in 1997, M. S. candidate. Her research interests include data security, privacy protection.Supported by:
陈学斌1,2,3(), 单丽洋1,2,3, 郭如敏1,2,3
通讯作者:
陈学斌
作者简介:
陈学斌(1970—),男,河北唐山人,教授,博士,CCF杰出会员,主要研究方向:大数据安全、物联网安全、网络安全 chxb@ncst.edu.cn基金资助:
CLC Number:
Xuebin CHEN, Liyang SHAN, Rumin GUO. Review of histogram publication methods based on differential privacy[J]. Journal of Computer Applications, 2024, 44(10): 3114-3121.
陈学斌, 单丽洋, 郭如敏. 基于差分隐私的直方图发布方法综述[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3114-3121.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023101520
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
AHP[ | 加噪后排序 | 准确性高 | 排序成本高 |
R-G-I[ | 梯度回归、离群点问题 | 发布结果准确性高 | 仅对含有离群点数据集有效 |
文献[ | 动态规划,指数机制 | 发布精度高 | ε随机分配、分组数确定受限 |
APB[ | 隐私预算分配权重优化模型,自适应分配 | 可用性提高 | 有序数据集优势不明显 |
SSHP[ | 指数机制、盘赌抽样,抽样排序和层次划分 | 数据可用性高 | 人为设定2个阈值 |
BDPP[ | 关联隐私泄露量化机制,多指标决策 | 均衡直接与间接隐私 | 模型复杂繁琐,推广性差 |
HPHP[ | 约束推断,动态规划 | 发布精度高 | ε小,约束推断统计值相同 |
NHPDP[ | 经验分布函数构建,组距大小设置隐私预算 | 避免“重拖尾、零桶” | 分组数先验知识计算 |
HP-SDP[ | 哈希编码混洗应答,堆排列,二次规划 | 发布精度高 | 应用场景单调 |
IKEM[ | K-means与指数机制 | 数据可用性高 | 人为设定聚类中心数 |
Tab. 1 Differential privacy histogram publishing algorithms for static data
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
AHP[ | 加噪后排序 | 准确性高 | 排序成本高 |
R-G-I[ | 梯度回归、离群点问题 | 发布结果准确性高 | 仅对含有离群点数据集有效 |
文献[ | 动态规划,指数机制 | 发布精度高 | ε随机分配、分组数确定受限 |
APB[ | 隐私预算分配权重优化模型,自适应分配 | 可用性提高 | 有序数据集优势不明显 |
SSHP[ | 指数机制、盘赌抽样,抽样排序和层次划分 | 数据可用性高 | 人为设定2个阈值 |
BDPP[ | 关联隐私泄露量化机制,多指标决策 | 均衡直接与间接隐私 | 模型复杂繁琐,推广性差 |
HPHP[ | 约束推断,动态规划 | 发布精度高 | ε小,约束推断统计值相同 |
NHPDP[ | 经验分布函数构建,组距大小设置隐私预算 | 避免“重拖尾、零桶” | 分组数先验知识计算 |
HP-SDP[ | 哈希编码混洗应答,堆排列,二次规划 | 发布精度高 | 应用场景单调 |
IKEM[ | K-means与指数机制 | 数据可用性高 | 人为设定聚类中心数 |
算法 | 误差 |
---|---|
AHP[ | |
APB[ | |
NHPDP[ | |
APS[ |
Tab. 2 Grouping errors of histogram publishing algorithms
算法 | 误差 |
---|---|
AHP[ | |
APB[ | |
NHPDP[ | |
APS[ |
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
SHP[ | 自适应抽样预测下一时刻,比较阈值大小 | 隐私预算少 | 随机抽样频率设定问题 |
DDHP[ | 比较L1、余弦和马氏距离选择最优测度 | 隐私预算分配少 | 数据量过大,效果不好 |
HPA-SW[ | 数据分块,区间近似估计 | 发布误差低 | 局部最优 |
IKFDP[ | 卡尔曼滤波,指数平滑改进突变性 | 数据可用性高 | 仅适用于含有突变数据集 |
APS[ | 近似计算预测 | 隐私预算少,数据可用性高 | 需要空间缓存数据 |
GGA[ | KL散度、贪婪群 | 数据可用性高 | 受更新率影响较大 |
ASDP-HPA[ | 时间衰减、自回归移动平均模型 | 误差少 | 分配隐私预算均分不适用 |
DPHP-DL[ | 动态数据流非等距直方图 | 隐私性和可用性高 | 时间复杂度较高 |
Tab. 3 Differential privacy histogram data publishing algorithm for streaming data
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
SHP[ | 自适应抽样预测下一时刻,比较阈值大小 | 隐私预算少 | 随机抽样频率设定问题 |
DDHP[ | 比较L1、余弦和马氏距离选择最优测度 | 隐私预算分配少 | 数据量过大,效果不好 |
HPA-SW[ | 数据分块,区间近似估计 | 发布误差低 | 局部最优 |
IKFDP[ | 卡尔曼滤波,指数平滑改进突变性 | 数据可用性高 | 仅适用于含有突变数据集 |
APS[ | 近似计算预测 | 隐私预算少,数据可用性高 | 需要空间缓存数据 |
GGA[ | KL散度、贪婪群 | 数据可用性高 | 受更新率影响较大 |
ASDP-HPA[ | 时间衰减、自回归移动平均模型 | 误差少 | 分配隐私预算均分不适用 |
DPHP-DL[ | 动态数据流非等距直方图 | 隐私性和可用性高 | 时间复杂度较高 |
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
LUE-DPtree[ | 异方差加噪 | 查询精度高 | 运行效率略差 |
LBLUE[ | 任意区间树、最优线性无偏估计 | 查询精度高、算法效率高 | 局部最优 |
CRTree[ | 伪完全k叉区间树 | 算法可行有效 | 小数据集优势不明显 |
RTP_MM[ | 对角矩阵 | 查询精度高 | 小数据集查询效率变化不明显 |
HQ_DPSAP[ | 历史查询、异方差加噪 | 支持任意区间计数查询 | 仅历史查询有规律的有效 |
CCDPSD[ | 异方差加噪、一致性约束 | 查询精度高、算法效率高 | 需人为设定分叉数和树高 |
HQ_RTPMM[ | 移动平均法预测查询范围 | 发布精度高 | 隐私预算无适合的划分 |
CA[ | 不需迭代 | 精度高、时间效率高 | 受数据集区间影响大 |
Tab. 4 Differential privacy histogram publishing algorithms oriented to interval tree structure
算法 | 原理 | 优点 | 缺点 |
---|---|---|---|
LUE-DPtree[ | 异方差加噪 | 查询精度高 | 运行效率略差 |
LBLUE[ | 任意区间树、最优线性无偏估计 | 查询精度高、算法效率高 | 局部最优 |
CRTree[ | 伪完全k叉区间树 | 算法可行有效 | 小数据集优势不明显 |
RTP_MM[ | 对角矩阵 | 查询精度高 | 小数据集查询效率变化不明显 |
HQ_DPSAP[ | 历史查询、异方差加噪 | 支持任意区间计数查询 | 仅历史查询有规律的有效 |
CCDPSD[ | 异方差加噪、一致性约束 | 查询精度高、算法效率高 | 需人为设定分叉数和树高 |
HQ_RTPMM[ | 移动平均法预测查询范围 | 发布精度高 | 隐私预算无适合的划分 |
CA[ | 不需迭代 | 精度高、时间效率高 | 受数据集区间影响大 |
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