信息安全研究 ›› 2018, Vol. 4 ›› Issue (9): 857-862.

• 法律法规 • 上一篇    下一篇

网络支付类账单过度采集个人数据引发的思考

汤艳君,钱丽纳,曾佩璇   

  1. 中国刑事警察学院
  • 收稿日期:2018-09-17 出版日期:2018-09-15 发布日期:2018-09-17
  • 通讯作者: 汤艳君
  • 作者简介:汤艳君(1966-),中国刑事警察学院教授、硕士研究生导师,主要从事电子数据取证研究。 钱丽纳(1993-),中国刑事警察学院2017级硕士研究生,主要研究方向为电子数据取证。 曾佩璇(1996-),中国刑事警察学院2017级硕士研究生,主要研究方向为电子数据取证。

Thoughts on the Excessive Collection of Personal Data by Online Payment Bills

  • Received:2018-09-17 Online:2018-09-15 Published:2018-09-17

摘要: 随着大数据技术和人工智能技术的发展和应用,企业的自主性在不断地增强,且对其用户数据的获取和分析程度也在逐渐加深.在此环境下,个人数据存在随时被泄露、窃取、滥用的风险.在详细阐述了个人数据概念的基础上,以支付宝年度账单为例引出个人数据被过度采集的现状及个人数据泄露事件,并且分析了支付宝数据采集模型.根据支付宝数据采集模型中的采集规则不明确、数据被过度披露、数据存储周期和存储量不明晰等方面的不足,从支付宝平台的安全技术研发、开发人员的安全意识、平台内部的数据安全监督机制、国内的相关法律法规及个人数据安全防范意识等方面提出针对模型的完善对策及数据采集的建议,从而为提高用户个人数据安全提供保障.

关键词: 支付宝, 年度账单, 采集模型, 个人数据, 完善对策

Abstract: With the development and application of big data technology and artificial intelligence technology, the autonomy of enterprises is constantly increasing, and the degree of acquisition and analysis of their user data is gradually deepening. In this environment, personal data is at risk of being leaked, stolen, and abused at any time. On the basis of expounding the concept of personal data, combined with Alipays annual billing event, the current situation of personal data being overcollected and personal data leakage incidents were extracted, and the Alipay data collection model was analyzed. According to the deficiencies in the collection rules of Alipay data collection model, the data is overdisclosed, the data storage cycle and the storage volume are not clear and so on, from the security technology development of Alipay platform, the security awareness of these developers, and the data security supervision mechanism within the platform, Domestic relevant laws and regulations and personal data security awareness and other aspects of the model to improve the countermeasures and data collection recommendations, so as to improve the security of personal data for users to provide protection.

Key words: Alipay, annual bill, acquisition model, personal data, improvement countermeasures