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摘要:
不同场景下数据类型和脱敏需求的差异，使得传统的数据脱敏方法难以满足大数据背景下的用户隐私保护需求。如何实现异构大数据中敏感信息的精准定向、高效脱敏，从而更好地确保数据安全、可信和可用，是本领域的研究难点。提出了一种在异构大数据环境下，基于文本、图片、音频和数据库等异构数据的脱敏模型，并对4个关键模块进行了描述。通过脱敏数据预处理，实现不同应用场景下敏感数据的自动标注和分级设置。采用数据预脱敏处理方法，并从数据可用性、数据关联性、隐私保护度、时间和空间复杂度等5个维度进行脱敏效果评价，实现定制化脱敏策略。经过脱敏任务调度完成脱敏任务分配和执行，并支持用户对部分脱敏数据恢复。基于提出的异构大数据脱敏模型，对2种典型数据脱敏应用场景进行了验证分析，表明所提模型能够实现不同应用场景下异构敏感数据的高效脱敏。
Abstract:Due to the variety of data types and desensitization demand in different scenarios, traditional data masking methods cannot meet the user privacy protection requirements in the environment of big data. How to realize the accurate pointing and efficient desensitization of heterogeneous big data for data security, trust and availability, has become the key in this area. In this paper, we propose a data masking model for heterogeneous big data applications, such as texts, images, voices and databases, and four key modules are presented in our model. First, the sensitive data automatic identification and classification in different applications are realized in different application scenarios by desensitization data preprocessing. Second, with data pre-masking method, the data masking evaluation is implemented in five dimensions, including data availability, data relevance, degree of privacy protection, and time and space complexity, to construct the customized desensitization strategy. Finally, after task scheduling, the allocation and execution of the data masking tasks are performed, and the masking data recovery can also be partially supported. Two typical data masking applications are verified and analyzed based on the proposed heterogeneous big data masking model, indicating that effective desensitization can be achieved in different application scenarios.
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表 1 不同数据类型的常用脱敏操作
Table 1. Commonly used data masking operation for different data types
操作类型 替换 改组 数字/日期差异 加密 删除 遮掩 平均值 数值 √ √ √ √ √ √ √ 中文字符 √ √ √ √ √ 英文字符 √ √ √ √ √ 特殊字符 √ √ √ √ √ 图片 √ √ √ 音频 √ √ 
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表 2 随机一致性指标RI的数值
Table 2. Values of random consistency indicator RI
n 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45
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