# Mdip1
###### Abstract  
As the Internet of Things (IoT) continues to expand, billions of IoT devices are now connected to the internet, producing vast quantities of data. Collecting and sharing this data has become crucial to improving IoT technologies and developing new applications. However, the publication of privacy-preserving IoT traffic data is exceedingly challenging due to the various privacy concerns surrounding users, IoT networks, and devices. In this paper, we propose a data transformation method aimed at safeguarding the privacy of IoT devices by transforming time series datasets. Based on our measurements, we have found that the transformed datasets retain the intrinsic value of the original IoT data and maintains data utility. This approach will enable non-expert data owners to better understand and evaluate the potential device-level privacy risks associated with their IoT data while simultaneously offering a reliable solution to mitigate their concerns about privacy violations.  
IoT; privacy leakage; time series; traffic pattern; data utility 20232023/10/10/2023/10  
patient [19], if unique traffic patterns in raw IoT data are not obfuscated. This can lead to privacy attacks by adversaries, even if the published datasets are anonymized.  
This paper addresses the threat of an IoT membership inference attack (MIA), where an attacker uses a released IoT dataset to determine whether it was generated by a specific IoT device. This can lead to further security attacks by the attacker. Our goal is to protect the privacy of IoT devices in raw IoT packet datasets by preventing inference of device types, while preserving the original data's inherent value. Current anonymization techniques [12; 13; 14; 15; 16; 17; 18] for IoT traffic packet traces focus mainly on protecting user privacy, with little emphasis on device-level sensitive information.  
This paper proposes a novel approach to protect device-level privacy in raw IoT packet data sharing. Our approach preserves device-level information while obfuscating sensitive patterns in the dataset, allowing data owners to mitigate the risk of privacy breaches in a cost-effective way. We use an efficient traffic reconstruction method to transform sensitive information while retaining useful data. Our results demonstrate that the proposed transformation method effectively obfuscates privacy-revealing patterns without compromising the value of the data. Machine learning models trained on the transformed datasets achieve similar accuracy to those trained on the original data, making the transformed datasets useful for data analytics tasks.  
The main contributions of our study are as follows:  
* We propose a novel method to protect device-level privacy in IoT data sharing by transforming time series datasets, preventing inference of IoT device types.
* We design an efficient traffic reconstruction method that preserves the value of the original data while protecting sensitive information. We evaluate the data utility of the transformed dataset using Euclidean distance and replicating studies, showing that our method effectively obfuscates privacy-revealing traffic patterns without sacrificing data utility.  
The rest of the paper is organized as follows. Related works are discussed in Section 2. Section 3 introduces IoT device Membership Inference Attack (MIA). In Section 4, we elaborate the proposed transformation method. The data utility of transformed data is evaluated in Section 5. The paper is concluded in Section 6.  
Figure 1: Unique and periodic packet size and IAT features of four surveillance cameras. **(a)** Packet size feature along with packet index; **(b)** IAT feature along with packet index.