- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Internet of things (IoT) is changing the way data is collected and processed. The scale and variety of devices, communication networks, and protocols involved in data collection present critical challenges for data processing and analyses. Newer and more sophisticated methods for data integration and aggregation are required to enhance the value of real-time and historical IoT data. Moreover, the pervasive nature of IoT data presents a number of privacy threats because of intermediate data processing steps, including data acquisition, data aggregation, fusion and integration. User profiling and record linkage are well studied topics in online social networks (OSNs); however, these have become more critical in IoT applications where different systems share and integrate data and information. The proposed study aims to discuss the privacy threat of information linkage, technical and legal approaches to address it in a heterogeneous IoT ecosystem. The paper illustrates and explains information linkage during the process of data integration in a smart neighbourhood scenario. Through this work, the authors aim to enable a technical and legal framework to ensure stakeholders awareness and protection of subjects about privacy breaches due to information linkage. © 2017 Nishtha Madaan, Mohd Abdul Ahad, Sunil M. Sastry. Published by Elsevier Ltd. All rights reserved.
5. Conclusions and future work
The given study presents an overview of privacy threats in emerging Internet of Things (IoT). It describes data management challenges for information and data integration in IoT ecosystems – presence of different metadata standards (interoperability), dynamicity of the data streams and availability of variety of smart devices (heterogeneity). The work presents an in-depth discussion and analysis on the privacy threat of Information Linkage during large-scale data integration in IoT ecosystems. It describes an example of a smart neighbourhood where data from various smart homes is integrated, and aggregated for a variety of services. Further, it details how some of these data processing tasks lead to unintended privacy breaches, such as identification of subjects and information linkage between their availability and resource use.
The main contribution of the proposed work is a distributed data integration algorithm for IoT ecosystems and resulting privacy threat of unintended information linkage. It then proposes technical and legal solutions to address this threat. The study argues that the scope of best practices of Privacy-byDesign is limited and is able to only partially address individual (and data) privacy. These principles often overlook the privacy breaches that occur during secondary level data processing and sharing tasks that do not directly involve raw data. The study emphasizes that this is an important area of research as most of the privacy threats in automated IoT ecosystems can surface at latter stages of data integration and aggregation where policies associated with raw data objects are of limited use. The given paper highlights the need for stakeholders’ understanding of data processing (and integration), its ramifications on their privacy preferences and corresponding legal policies to protect their rights over data-use. As a future work, the authors aim to write a formal technical-legal framework to address privacy concerns during horizontal data integration both at device and data-stream level in heterogeneous IoT ecosystems.