Security Management Suitable for Lifecycle of Personal Information in Multi-User IoT Environment

In recent years, as all actions of Internet users become information, the importance of personal information is emphasized, but in reality, the management of personal information is still insufficient. With the advent of the concept of sharing systems such as the sharing economy, the numbers of IoT application services (for example, a healthcare service using sharing IoT devices, or a vehicle sharing system with IoT devices) using users’ personal information are increasing, but the risk of using personal information is not managed. To solve this issue, the European GDPR stipulates the content of personal information protection. In this paper, we present a method to securely manage personal information in IoT devices in IoT application environments in accordance with the GDPR. We first describe the lifecycle stages of personal information occurring in IoT application services and propose a method to securely manage personal information at each stage of the lifecycle according to the flow of personal information in IoT devices. We also evaluated the usefulness and applicability of the proposed scheme through two service scenarios. Since the proposed method satisfies the requirements for personal information management in IoT application environments, it is expected to contribute to the development of the IoT business field that handles personal information.

China's Regulatory Framework for Dynamic and Personalized Pricing in the Digital Economy

Under the Chinese legal system, in principle, there is no objection to dynamic and personalized pricing of enterprises. Dynamic pricing does not involve the processing of personal information, and consumers have a higher perception of price fairness, it is seldom concerned by laws and policies. Personalized pricing involves the processing of personal information, and consumers generally feel that the price is unfair and difficult to accept, so it is the focus of legal regulation. Enterprises face three obstacles in implementing personalized pricing. First, in terms of personal information protection, enterprises should abide by the provisions on personal consent and automatic decision-making in the Personal Information Protection Law. Second, in the aspect of anti-monopoly, enterprises should abide by the provisions of the Anti-Monopoly Law, and cannot achieve collusion through algorithms and abuse market dominance to implement differential treatment for trading counterparts. Third, in the protection of consumers' rights and interests, enterprises should respect the Price Law and other laws, and cannot commit price fraud and price discrimination. The current law on dynamic and personalized pricing is not perfect. In the future, we can protect consumers' rights and interests mainly by strengthening enterprises' obligation of providing information.

View analysis of personal information leakage and privacy protection in big data era—based on Q method

PurposeIn the era of big data, people are more likely to pay attention to privacy protection with facing the risk of personal information leakage while enjoying the convenience brought by big data technology. Furthermore, people’s views on personal information leakage and privacy protection are varied, playing an important role in the legal process of personal information protection. Therefore, this paper aims to propose a semi-qualitative method based framework to reveal the subjective patterns about information leakage and privacy protection and further provide practical implications for interested party.Design/methodology/approachQ method is a semi-qualitative methodology which is designed for identifying typologies of perspectives. In order to have a comprehensive understanding of users’ viewpoints, this study incorporates LDA & TextRank method and other information extraction technologies to capture the statements from large-scale literature, app reviews, typical cases and survey interviews, which could be regarded as the resource of the viewpoints.FindingsBy adopting the Q method that aims for studying subjective thought patterns to identify users’ potential views, the authors have identified three categories of stakeholders’ subjectivities: macro-policy sensitive, trade-offs and personal information sensitive, each of which perceives different risk and affordance of information leakage and importance and urgency of privacy protection. All of the subjectivities of the respondents reflect the awareness of the issue of information leakage, that is, the interested parties like social network sites are unable to protect their full personal information, while reflecting varied resistance and susceptibility of disclosing personal information for big data technology applications.Originality/valueThe findings of this study provide an overview of the subjective patterns on the information leakage issue. Being the first to incorporate the Q method to study the views of personal information leakage and privacy protection, the research not only broadens the application field of the Q method but also enriches the research methods for personal information protection. Besides, the proposed LDA & TextRank method in this paper alleviates the limitation of statements resource in the Q method.

Keyword Detection Based on RetinaNet and Transfer Learning for Personal Information Protection in Document Images

In this paper, a keyword detection scheme is proposed based on deep convolutional neural networks for personal information protection in document images. The proposed scheme is composed of key character detection and lexicon analysis. The first part is the key character detection developed based on RetinaNet and transfer learning. To find the key characters, RetinaNet, which is composed of convolutional layers featuring a pyramid network and two subnets, is exploited to detect key characters within the region of interest in a document image. After the key character detection, the second part is a lexicon analysis, which analyzes and combines several key characters to find the keywords. To train the model of RetinaNet, synthetic image generation and data augmentation are exploited to yield a large image dataset. To evaluate the proposed scheme, many document images are selected for testing, and two performance measurements, IoU (Intersection Over Union) and mAP (Mean Average Precision), are used in this paper. Experimental results show that the mAP rates of the proposed scheme are 85.1% and 85.84% for key character detection and keyword detection, respectively. Furthermore, the proposed scheme is superior to Tesseract OCR (Optical Character Recognition) software for detecting the key characters in document images. The experimental results demonstrate that the proposed method can effectively localize and recognize these keywords within noisy document images with Mandarin Chinese words.