Research on Data Noise Addition Method Based on Availability

doi:10.3969/j.issn.1671-1122.2024.11.012

Abstract

Abstract:

As information technology rapidly advances, data privacy protection has become a focal point of interest. Effectively safeguarding personal privacy while maximizing the utility of data resources is an urgent issue to address. Implementing data privacy protection through noise addition is one of the prevalent methods, yet research on the impact of various noise addition techniques on data usability is scarce. This study experimentally introduced laplace noise and gaussian noise to “five-star” rating data indicative of user experience. It compared and analyzed the alterations in three statistical metrics of the noise data: mean absolute error, root mean square error, and variance growth rate. The paper further investigated the effects of combining noise with different ratios and varying data volumes on statistical indicators. The experimental results indicate that with larger data volumes, a higher proportion of Gaussian noise addition results in statistical properties of the noise data that more closely resemble the original data. This approach ensures data usability while achieving personal privacy protection.

Key words: privacy protection, availability, evaluation data, Laplace noise, Gaussian noise

CLC Number:

TP309

GU Haiyan, LIU Qi, MA Zhuo, ZHU Tao, QIAN Hanwei. Research on Data Noise Addition Method Based on Availability[J]. Netinfo Security, 2024, 24(11): 1731-1738.

Figures/Tables 13

References 19

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用户编号	原始评分	添加噪声后的评分	添加噪声后数据的偏差
1	4	3.909	0.091
2	4	4.007	-0.007
3	4	4.194	-0.194
…	…	…	…
99	5	4.866	0.134
100	4	4.401	-0.401

用户编号	原始评分	添加噪声后的评分	添加噪声后数据的误差
1	4	4.048	-0.048
2	4	4.122	-0.122
3	4	4.074	-0.074
…	…	…	…
99	5	5.252	-0.252
100	4	3.979	0.021

数据类型	原始数据	添加拉普拉斯噪声后的数据	添加高斯噪声后的数据
平均值	4.340	4.357	4.346
标准差	0.790	0.784	0.800
均方差	0.624	0.615	0.640
平均绝对误差	—	0.126	0.095
均方根误差	—	0.171	0.117
方差增长率	—	1.44%	2.56%

数据类型	原始数据	添加拉普拉斯噪声的数据	添加高斯噪声的数据	添加组合噪声的数据
平均值	4.340	4.357	4.346	4.351
标准差	0.790	0.784	0.800	0.798
均方差	0.624	0.615	0.640	0.637
平均绝对误差	—	0.126	0.095	0.092
均方根误差	—	0.171	0.117	0.118
方差增长率	—	1.44%	2.56%	2.08%

添加高斯噪声的比例η	平均绝对误差	均方根误差	方差增长率
0	0.121	0.172	3.06%
5%	0.122	0.167	3.01%
10%	0.120	0.165	2.69%
15%	0.119	0.164	2.52%
20%	0.118	0.162	3.04%
25%	0.118	0.162	4.48%
30%	0.115	0.159	5.94%
35%	0.115	0.157	5.41%
40%	0.116	0.158	5.70%
45%	0.112	0.152	2.19%
50%	0.113	0.153	2.80%
55%	0.108	0.149	4.07%
60%	0.106	0.146	3.13%
65%	0.100	0.136	1.97%
70%	0.097	0.132	1.54%
75%	0.099	0.134	1.83%
80%	0.096	0.125	0.47%
85%	0.100	0.128	0.59%
90%	0.100	0.127	0.91%
95%	0.096	0.120	1.60%
100%	0.090	0.117	0.96%