The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things

Abstract

In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

“…In a study on Sybil detection in industrial WSNs [175], a kerneloriented scheme was proposed to differentiate Sybil attackers from normal sensors by clustering the channel vectors. A clustering algorithm showed the potential to preserve private data anonymisation in an IoT system [176]. The use of clustering to develop data anonymisation algorithms can significantly advance data exchange security [176].…”

A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era

“…Nonetheless, without the support of policies, processes, and people, the implementation of only data anonymization will be insufficient. Some companies fairly managed to implement data anonymization on a small scale using SQL scripts efficiently to encrypt data [152]. Some other companies have failed after obtaining the best data-masking tool [153].…”

“…However, these techniques can be applied only on at-rest or visible data i.e. logs, data exports, web pages [152], [153].…”

MSFA: Multiple System Fingerprint Attack Scheme for IoT Anonymous Communication

“…The experiment first groups the captured traffic data according to IP which must be the routing nodes of the Tor to obtain valuable packet information. In the experiment, after the traffic data is obtained, it only needs to request and respond to the packet size as it 6 Security and Communication Networks (1) class Filter{ (2) public: //Defines the storage server IP address; server ips (3) unordered set <string> server ips; //Defines the storage client IP address: client ips (4) unordered set <string> client ips; //Defines the maximum port that needs to be processed (5) int PROXYPORT MIN; //Defines the minimum port that needs to be processed (6) int PROXYPORT MAX; (7) public: //FilterConstructor (8) Filter(char * clientipfname, char * serveripfname, int portmin, int portmax); //Read the IP function (9) int read ips(unordered set<string>&set, char * fname); //Determines whether the file loads the function (10) bool is onload(u char * payload); //Determine whether the traffic function is listening (11) bool is monitoredtraffic(char * src, unsigned int sport, char * dst, unsigned int dport); //Implement the transformation function for the data (12) RETparse one(char * capfname, int proxy port min, intproxy port max, int remove ack, char * monitoredoutname, char * localoutname, char * c2stau, char * s2ctau, char * timeseq); (13) }; contains more complex data. Therefore, the original data needs to be processed.…”

“…Amin et al [2] proposed the architecture of a patient monitoring system in WMSN (wireless medical sensor network) and designed an anonymous mutual authentication protocol suitable for mobile users to provide secure access and privacy for patient data. Mingshan Xie et al [3] proposed the anonymization protection algorithm which is suitable for the data exchange in an incompletely open manner for the ego of data in the IoT. The anonymous dataset generated by the algorithm can effectively protect the sensitive information of IoT under the premise of ensuring the availability of the data.…”