پذیرفته شده برای ارائه شفاهی ، صفحه 9-23 (15) اصل مقاله (841.9 K)

¹غزاله سادات میرهاشمی ؛ ²محمدرضا کیوان پور

¹دانشگاه الزهرا تهران

²دانشگاه الزهرا ، مدیر گروه و استاد دانشکده فنی و مهندسی

Recently, the amount of information is growing exponentially, so security and privacy protection have been a public concern for quite a long time. This data can be utilized in several fields, such as business, health care, and cybersecurity. Cyberspace is a virtual computer environment to ease online communication. Data mining applications can detect future cyber-attacks through analysis. Data mining techniques bring a number of privacy risks while also allowing users to access information that was previously hidden. However, there are various techniques and algorithms for data mining that preserve cyberspace and privacy for publishing data in data mining. These algorithms consist of perturbation and anonymization. In this paper, a framework has been developed for analyzing qualitative methods as a platform for data classification and evaluation based on the latest perspectives. Our aim is to present a systematic review of data dissemination methods to prevent cyber-attacks and privacy preserving data mining (PPDM) and provide a platform for qualitative comparison within this framework. Additionally, exposing existing method weaknesses is important for improving PPDM approaches and determining the appropriate methods according to the requirements of the fields to be studied.

cyber security؛ privacy preserving data mining؛ data publishing؛ perturbation؛ anonymization