| 1 | An Efficient and Privacy-Preserving Data Aggregation Scheme Supporting Arbitrary Statistical Functions in IoT显示文摘The Internet of Things(IoT)has profoundly impacted our lives and has greatly revolutionized our lifestyle.The terminal devices in an IoT data aggregation application sense real-time data for the remote cloud server to achieve intelligent decisions.However,the high frequency of collecting user data will raise people concerns about personal privacy.In recent years,many privacy-preserving data aggregation schemes have been proposed.Unfortunately,most existing schemes cannot support either arbitrary aggregation functions,or dynamic user group management,or fault tolerance.In this paper,we propose an efficient and privacy-preserving data aggregation scheme.In the scheme,we design a lightweight encryption method to protect the user privacy by using a ring topology and a random location sequence.On this basis,the proposed scheme supports not only arbitrary aggregation functions,but also flexible dynamic user management.Furthermore,the scheme achieves faulttolerant capabilities by utilizing a future data buffering mechanism.Security analysis reveals that the scheme can achieve the desired security properties,and experimental evaluation results show the scheme's efficiency in terms of computational and communication overhead. | Haihui Liu Jianwei Chen Liwei Lin Ayong Ye Chuan Huang | 2022 | China Communications2022，19，6： | 0 |
| 2 | A New Edge Perturbation Mechanism for Privacy-Preserving Data Collection in IOT显示文摘A growing amount of data containing the sensitive information of users is being collected by emerging smart connected devices to the center server in Internet of things(IoT)era,which raises serious privacy concerns for millions of users.However,existing perturbation methods are not effective because of increased disclosure risk and reduced data utility,especially for small data sets.To overcome this issue,we propose a new edge perturbation mechanism based on the concept of global sensitivity to protect the sensitive information in IoT data collection.The edge server is used to mask users’sensitive data,which can not only avoid the data leakage caused by centralized perturbation,but also achieve better data utility than local perturbation.In addition,we present a global noise generation algorithm based on edge perturbation.Each edge server utilizes the global noise generated by the center server to perturb users’sensitive data.It can minimize the disclosure risk while ensuring that the results of commonly performed statistical analyses are identical and equal for both the raw and the perturbed data.Finally,theoretical and experimental evaluations indicate that the proposed mechanism is private and accurate for small data sets. | CHEN Qiuling YE Ayong ZHANG Qiang HUANG Chuan | 2023 | Chinese Journal of Electronics2023，32，3： | 0 |
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