用户认知联接视角下情境叙事型政府开放数据隐私风险评估研究

doi:10.13998/j.cnki.issn1002-1248.25-0322

Abstract

Abstract:

[Purpose/Significance] Open government data (OGD) has increasingly adopted storytelling elements to improve public engagement and enhance user comprehension. Although this narrative approach enhances data accessibility and cognitive resonance, it raises significant privacy concerns. Specifically, storytelling may activate users' cognitive schemas, enabling them to infer sensitive personal information even from anonymized datasets. This dual effect between data usefulness and privacy risk is becoming an increasing challenge for data providers and policymakers. In this study, we aim to explore how storytelling in OGD affects users' cognitive reasoning processes and leads to privacy risks. Our work innovatively combines cognitive psychology, information science, and privacy risk assessment. This interdisciplinary approach offers a new perspective on how data narratives shape inference behavior. Distinct from existing research, this paper focuses on how cognitive mechanisms driven by storytelling influence users' perception and extraction of private information. This research holds practical significance for designing privacy-aware data disclosure strategies that strike a balance between openness and protection. [Method/Process] In order to analyze the cognitive mechanisms underlying privacy risk, we adopted a mixed-methods research design grounded in relevance theory, schema theory, and the S-O-R model. We first constructed a user cognitive connection model that conceptualized how narrative stimuli activated cognitive processing and led to privacy-related inferences. Based on this model, we developed a privacy risk assessment index comprising three primary dimensions: data association and reasoning, data processing and decoding, and implicit suggestion and implication. We then conducted a controlled experiment involving 236 participants, who were randomly divided into a storytelling group and a non-storytelling group. To analyze the collected data, we used the CRITIC method to assign objective weights to evaluation indicators and applied a fuzzy comprehensive evaluation method to quantify and compare privacy risks across groups. [Results/Conclusions] Our results demonstrated that storytelling significantly heightened users' ability to infer sensitive personal information. The average inference score in the storytelling group was significantly higher than that in the non-storytelling group (p<0.05), and the comprehensive privacy risk level was rated as "medium risk" compared to the non-storytelling group's "low risk." Across all three risk dimensions, the storytelling group consistently exhibited greater cognitive engagement and higher potential for privacy exposure. These findings suggested that while storytelling enhanced user understanding, it also increased the risk of privacy violations. As such, we recommended that government data platforms adopt non-storytelling or partially abstracted data presentation strategies to reduce risk while preserving clarity. From a policy perspective, we advocated for the integration of intelligent narrative-generation algorithms and privacy-by-design principles to protect users' information. Although limited by sample size and data diversity, this study offered a foundation for future research into the cognitive underpinnings of privacy risk. Further work may explore other forms of storytelling, demographic influences on inference behavior.

Key words: government open data, situational narrative, user-perceived connection, privacy risk, control experiment

CLC Number:

G252

GENG Ruili, WANG Yifan, LI Sentao, WEI Qi. Privacy Risk of Government Open Data Management from the Storytelling Perspective of the User-Cognitive Connection[J].Journal of library and information science in agriculture, 2025, 37(6): 20-36.

Figures/Tables 7

Fig.1

Table 1

Table 2

Table 3

Table 4

Table5

Table 6

Experimental results"

组别	风险因素	模糊综合评价结果	隐私风险等级
对照组	数据关联和推理	$(0.171,0.391,0.265,0.173,0.000)$	较低风险
	数据加工和解码	$(0.133,0.352,0.345,0.170,0.000)$	较低风险
	信息暗示和隐含	$(0.068,0.336,0.311,0.226,0.059)$	较低风险
实验组	数据关联和推理	$(0.000,0.104,0.412,0.421,0.063)$	中等风险
	数据加工和解码	$(0.000,0.047,0.306,0.501,0.146)$	中等风险
	信息暗示和隐含	$(0.000,0.033,0.272,0.509,0.186)$	中等风险

Table 6

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一级指标	二级指标	指标描述	参考来源
数据关联和推理	关联推理程度	我可以通过实验材料中的描述将其中的部分情节或事件进行关联并加以推理	[14]
	隐私泄露可能性	我认为对实验材料进行关联推理后，有可能泄露自然人的个人敏感信息	作者自拟
	认知联接复杂度	我认为通过实验材料推理出其他相关个人敏感信息并不困难
	推理准确性	我可以准确推理出实验材料中个人敏感信息
	推理速度	我可以在短时间内推理出材料中隐含的相关个人敏感信息
数据加工和解码	敏感信息获取	我可以识别出实验材料中隐含的个人敏感信息	[27]
	解码过程复杂度	我认为识别出实验材料中隐含的个人敏感信息并不困难	[49]
	加工过程中隐私暴露	我在处理实验材料时发现，其中隐含的个人敏感信息能被提取到	[49]
	隐私信息提取速度	我可以在短时间内提取到实验材料中的个人隐私信息	作者自拟
	加工后信息关联度	我认为实验材料与个人隐私信息的关联度高	作者自拟
信息暗示和隐含	暗示识别能力	我认为实验材料隐含了其他的敏感信息	[33]
	隐私揭示程度	我可以通过实验材料的隐含信息揭示相关个人隐私信息
	隐含信息提取准确性	我可以准确地提取隐含的其他相关个人敏感信息
	信息加工的隐私保护措施	我认为实验材料没有采取隐私保护措施
	信息敏感性	我认为实验材料中的某些信息具备隐私敏感性，易与个人隐私信息关联	[26]

评估值	风险等级	定义
0~0.2	微弱风险	风险发生后用户遭受轻微损失
0.2~0.4	较低风险	风险发生后用户遭受较低损失
0.4~0.6	中等风险	风险发生后用户遭受一般损失
0.6~0.8	较高风险	风险发生后用户遭受较高损失
0.8~1	高危风险	风险发生后用户遭受巨大损失

项目	类别	实验组/人	对照组/人	总计/人
性别	男	63	62	125
性别	女	55	56	111
年龄	18~25岁	76	73	149
	26~35岁	30	38	68
	36~45岁	10	5	15
	45岁以上	2	2	4
学历	高中及以下	4	5	9
	专科	21	26	47
	本科	55	42	97
	硕士	34	41	75
	博士	4	4	8
职业	政府工作人员	6	7	13
	教师、科研工作者	8	6	14
	学生	68	69	137
	个体经营者	3	6	9
	其他	33	30	63

组别	样本数	均值	最大值	最小值	标准偏差	标准误差	均值95%置信区间
组别	样本数	均值	最大值	最小值	标准偏差	标准误差	下限	上限
实验组	118	5.136	8	2	1.132	0.104	4.929	5.342
对照组	118	2.568	5	1	0.901	0.083	2.404	2.732
总计	236	3.852	8	1	1.642	0.107	3.642	4.061

项目	平方和	自由度	均方	F	显著性
组间	389.021	1	389.021	371.877	0.000
组内	244.788	234	1.046
总计	633.809	235

Privacy Risk of Government Open Data Management from the Storytelling Perspective of the User-Cognitive Connection

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[3]	DAI Xinwei, LI Feng. Enlightenment of AI Literacy Educational Designs and Practices at Japanese MDASH Literacy-level Approved Universities [J]. Journal of library and information science in agriculture, 2025, 37(5): 86-101.
[4]	SHI Xujie, YUAN Fan, LI Jia. Searching as Learning in the Context of Generative Artificial Intelligence: Technological Pathways, Behavioral Evolution, and Ethical Challenges [J]. Journal of library and information science in agriculture, 2025, 37(5): 40-57.
[5]	XIAO Keyi, LI Yunfan, CHEN Yingying, PENG Xi. User Experience of Public Library Intelligent Service from the Perspective of Actor Network Theory [J]. Journal of library and information science in agriculture, 2025, 37(4): 51-65.
[6]	TAN Miao, DAI Mengfei. Empowered Digital Reading Promotion of Historical Documents with Generative AI [J]. Journal of library and information science in agriculture, 2025, 37(4): 83-93.
[7]	GOU Ruike, LUO Wei. Influencing Factors of Continuous Use Intention of "Generation Z" Users of an AIGC Platform [J]. Journal of library and information science in agriculture, 2025, 37(3): 66-80.
[8]	SANG Yuanyuan. Multimodal Learning Technology Aimed at Exploring the Innovative Path of Library Intelligence Service [J]. Journal of library and information science in agriculture, 2025, 37(3): 42-52.
[9]	ZHANG Xingwang, LI Jie, LI Sifan, WANG Xiaopei. Theoretical Model, Model Innovation, and Important Implications of DeepSeek Empowering Library Knowledge Services [J]. Journal of library and information science in agriculture, 2025, 37(1): 4-16.
[10]	SHI Qin, XIE Jing, WU Shang. Influencing Factors and Correlations of User Satisfaction with Mobile Health Applications [J]. Journal of library and information science in agriculture, 2025, 37(1): 33-46.
[11]	ZHANG Mingjie, ZHAO Ruixue. Emotion Perception and Service Optimization in ChatGPT-Driven Smart Libraries [J]. Journal of library and information science in agriculture, 2024, 36(12): 74-88.
[12]	Chaochen WANG, Ayang QI, Xiaoqing XU, Linwei CUI. Group Socialization of Digital Games to Encourage Users' Autonomous Reading Behavior and Content Backtracking: Based on the Domestic Console Game “Black Myth: WUKONG” Known Outside of Fixed Circle [J]. Journal of library and information science in agriculture, 2024, 36(11): 64-78.
[13]	Kun HUANG, Qionghao GUO, Chenyu ZHOU, Lei LI. Information Demand and Supply of Rural Elderly: Based on a Field Investigation of Village C, Taiyuan City, Shanxi Province [J]. Journal of library and information science in agriculture, 2024, 36(11): 4-19.
[14]	Yan MOU. Social Media Users' Expression, Formation Mechanism, and Coping Strategies for Algorithm Aversion [J]. Journal of library and information science in agriculture, 2024, 36(10): 86-94.
[15]	Zheng WANG, Miao ZHUANG, Yudi ZHANG, Yaqi ZHANG. Factors Influencing Online Health Information Acquisition Behavior of Rural Elderly Groups in Western China: A Field Study from the Guanzhong Region of Shaanxi Province [J]. Journal of library and information science in agriculture, 2024, 36(10): 23-37.