الفهرس 
Block nested loop (BNL) skyline algorithmsOnly 14 pages are availabe for public view
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Abstract
Fore and foremost with the fast growth of data, researchers have begun to focus on how to use it most effectively for decision-making in a variety of new applications. These vast amounts of data are incredibly useful and valuable for scientific research, commercial productivity, and human advancement. Indeed, It benefits everything from government to business, healthcare to better navigation, smart cities to national security. There are more possibilities to work even though the problems of dealing with these data are also greater. Big data challenges have been considered in this research.
The veracity, volume, and velocity of data all contribute to these difficulties. Nonetheless, if we can address the difficulties surrounding big data, we may see an improvement in the superiority of our lives. The thesis focuses on privacy, which is one of the most pressing concerns with big data. The privacy challenge, privacy violations, and privacy protection strategies are the main goal of this research. Previous strategies will be examined to identify and eliminate their flaws, and a robust and widely applicable privacy protection technique will be proposed.
There are numerous approaches for protecting privacy in large data, as discussed in the literature review section below. For ensuring privacy in huge data, the thesis introduces a mechanism called a Special Negative Database (SNDB). SNDB is presented as a way to circumvent all of the preceding approaches’ flaws. SNDB considers all forms of attributes, including binomial, numeric, date, time, and polynomial.
SNDB works by replacing only sensitive attributes with their complements, misleading unauthorized users and hackers. Consequently, an unauthorized user will be unable to distinguish between the original data and the data after using this technique. The level of deception for unauthorized users is the most significant factor in privacy protection.
Clearly, SNDB has the advantage of high privacy protection in big data, which allows it to avoid the drawbacks of prior approaches. Because it solely deals with sensitive attributes, SNDB is not time-demanding. Because there is no decrease or increase for every record of data, it also keeps track of data integrity and size, which makes SNDB ideal for huge data. It similarly has a modest level of complexity because it simply uses its complement to replace sensitive attribute values. We can easily obtain the original data after using SNDB by applying the complement again according to the data owner’s criteria.