Advancing Data Privacy: A Novel K-Anonymity Algorithm with Dissimilarity Tree-Based Clustering and Minimal Information Loss

Anonymization serves as a crucial privacy protection technique employed across various technology domains, including cloud storage, machine learning, data mining and big data to safeguard sensitive information from unauthorized third-party access. As the significance and volume of data grow exponentially, comprehensive data protection against all threats is of utmost importance. The main objective of this paper is to provide a brief summary of techniques for data anonymization and differential privacy.A new k-anonymity method, which deviates from conventional k-anonymity approaches, is proposed by us to address privacy protection concerns. Our paper presents a new algorithm designed to achieve k-anonymity through more efficient clustering. The processing of data by most clustering algorithms requires substantial computation. However, by identifying initial centers that align with the data structure, a superior cluster arrangement can be obtained.Our study presents a Dissimilarity Tree-based strategy for selecting optimal starting centroids and generating more accurate clusters with reduced computing time and Normalised Certainty Penalty (NCP). This method also has the added benefit of reducing the Normalised Certainty Penalty (NCP). When compared to other methods, the graphical performance analysis shows that this one reduces the amount of overall information lost in the dataset being anonymized by around 20% on average. In addition, the method that we have designed is capable of properly handling both numerical and category characteristics

Generic CBTS: Correlation based Transformation Strategy for Privacy Preserving Data Mining

Mining useful knowledge from corpus of data has become an important application in many fields. Data Mining algorithms like Clustering, Classification work on this data and provide crisp information for analysis. As these data are available through various channels into public domain, privacy for the owners of the data is increasing need. Though privacy can be provided by hiding sensitive data, it will affect the Data Mining algorithms in knowledge extraction, so an effective mechanism is required to provide privacy to the data and at the same time without affecting the Data Mining results. Privacy concern is a primary hindrance for quality data analysis. Data mining algorithms on the contrary focus on the mathematical nature than on the private nature of the information. Therefore instead of removing or encrypting sensitive data, we propose transformation strategies that retain the statistical, semantic and heuristic nature of the data while masking the sensitive information. The proposed Correlation Based Transformation Strategy (CBTS) combines Correlation Analysis in tandem with data transformation techniques such as Singular Value Decomposition (SVD), Principal Component Analysis (PCA) and Non Negative Matrix Factorization (NNMF) provides the intended level of privacy preservation and enables data analysis. The proposed technique will work for numerical, ordinal and nominal data. The outcome of CBTS is evaluated on standard datasets against popular data mining techniques with significant success and Information Entropy is also accounted

A Data Mining Perspective in Privacy Preserving Data Mining Systems

Privacy Preserving Data Mining () presents a novel framework for extracting and deriving information when the data is distributed amongst the multiple parties. The privacy preservation of data and the use of efficient data mining algorithms in systems is a major issue that exists. Most of the existing systems employ the cryptographic key exchange process and the key computation process accomplished by means of certain trusted server or a third party. To eliminate the key exchange and key computation overheads this paper discusses the Key Distribution-Less Privacy Preserving Data Mining () system. The novelty of the system is that no data is published but only the association rules are published to achieve effective data mining results. The embodies the data mining algorithm for classification rule generation and data mining. The results discussed in this paper compare the based system with the based system and the efficiency in rule generation, overhead reduction and classification efficiency of the latter is proved

Are anonymity-seekers just like everybody else? An analysis of contributions to Wikipedia from Tor

User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor because of a perception that proxies are a source of vandalism, spam, and abuse. Although these blocks might be effective, collateral damage in the form of unrealized valuable contributions from anonymity seekers is invisible. One of the largest and most important user-generated content sites, Wikipedia, has attempted to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful. We draw upon several data sources and analytical techniques to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to those from other groups of Wikipedia users. Our analysis suggests that although Tor users who slip through Wikipedia's ban contribute content that is more likely to be reverted and to revert others, their contributions are otherwise similar in quality to those from other unregistered participants and to the initial contributions of registered users.Comment: To appear in the IEEE Symposium on Security & Privacy, May 202

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Effects of Information Filters: A Phenomenon on the Web

In the Internet era, information processing for personalization and relevance has been one of the key topics of research and development. It ranges from design of applications like search engines, web crawlers, learning engines to reverse image searches, audio processed search, auto complete, etc. Information retrieval plays a vital role in most of the above mentioned applications. A part of information retrieval which deals with personalization and rendering is often referred to as Information Filtering. The emphasis of this paper is to empirically analyze the information filters commonly seen and to analyze their correctness and effects. The measure of correctness is not in terms of percentage of correct results but instead a rational approach of analysis using a non mathematical argument is presented. Filters employed by Google’s search engine are used to analyse the effects of filtering on the web. A plausible

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET)

Cryptographic approaches are traditional and preferred methodologies used to preserve the privacy of data released for analysis. Privacy Preserving Data Mining (PPDM) is a new trend to derive knowledge when the data is available with multiple parties involved. The PPDM deployments that currently exist involve cryptographic key exchange and key computation achieved through a trusted server or a third party. The key computation over heads, key compromise in presence of dishonest parties and shared data integrity are the key challenges that exist. This research work discusses the provisioning of data privacy using commutative RSA algorithms eliminating the overheads of secure key distribution, storage and key update mechanisms generally used to secure the data to be used for analysis. Decision Tree algorithms are used for analysis of the data provided by the various parties involved. We have considered the C5. 0 data mining algorithm for analysis due to its efficiency over the currently prevalent algorithms like C4. 5 and ID3. In this paper the major emphasis is to provide a platform for secure communication, preserving privacy of the vertically partitioned data available with the parties involved in the semi-honest trust model. The proposed Key Distribution-Less Privacy Preserving Data Mining () model is compared with other protocols like Secure Lock and Access Control Polynomial to prove its efficiency in terms of the computational overheads observed in preserving privacy. The experiential evaluations proves the reduces the computational overheads by about 95.96% when compared to the Secure Lock model and is similar to the