دانلود مقاله انگلیسی رایگان:کاوش مجموعه موارد تکراری حفظ حریم خصوصی: حداکثر سازی تسهیل داده ها براساس بازسازی بانک اطلاعاتی - 2019
بلافاصله پس از پرداخت دانلود کنید
دانلود مقاله انگلیسی داده کاوی رایگان
  • Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction
    Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction


    ترجمه فارسی عنوان مقاله:

    کاوش مجموعه موارد تکراری حفظ حریم خصوصی: حداکثر سازی تسهیل داده ها براساس بازسازی بانک اطلاعاتی


    منبع:

    Sciencedirect - Elsevier - Computers & Security, 84 (2019) 17-34: doi:10:1016/j:cose:2019:03:008


    نویسنده:

    Shaoxin Li a , Nankun Mu a , Junqing Le a , Xiaofeng Liao b , ∗


    چکیده انگلیسی:

    The process of frequent itemset mining (FIM) within large-scale databases plays a significant part in many knowledge discovery tasks, where, however, potential privacy breaches are possible. Privacy preserving frequent itemset mining (PPFIM) has thus drawn increasing attention recently, where the ultimate goal is to hide sensitive frequent itemsets (SFIs) so as to leave no confidential knowledge uncovered in the resulting database. Nevertheless, the vast majority of the proposed methods for PPFIM were merely based on database per- turbation, which may result in a significant loss of data utility in order to conceal all SFIs. To alleviate this issue, this paper proposes a database reconstruction-based algorithm for PPFIM (DR-PPFIM) that can not only achieve a high degree of privacy but also afford a rea- sonable data utility. In DR-PPFIM, all SFIs with related frequent itemsets are first identified for removing in the pre-sanitize process by implementing a devised sanitize method. With the remained frequent itemsets, a novel database reconstruction scheme is proposed to re- construct an appropriate database, where the concepts of inverse frequent itemset mining (IFIM) and database extension are efficiently integrated. In this way, all SFIs are able to be hidden under the same mining threshold while maximizing the data utility of the synthetic database as much as possible. Moreover, we also develop a further hiding strategy in DRPPFIM to further decrease the significance of SFIs with the purpose of reducing the risk of disclosing confidential knowledge. Extensive comparative experiments are conducted on real databases to demonstrate the superiority of DR-PPFIM in terms of maximizing the utility of data and resisting potential threats.
    Keywords: Privacy preserving data mining | Frequent itemset | Database reconstruction | Inverse frequent itemset mining | Database extension


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 18
    حجم فایل: 1312 کیلوبایت

    قیمت: رایگان


    توضیحات اضافی:




اگر این مقاله را پسندیدید آن را در شبکه های اجتماعی به اشتراک بگذارید (برای به اشتراک گذاری بر روی ایکن های زیر کلیک کنید)

تعداد نظرات : 0

الزامی
الزامی
الزامی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi