Privacy preserving classification over differentially private data

Imagine numerous clients, each with personal data; individual inputs are severely corrupt, and a server only concerns the collective, statistically essential facets of this data. In several data mining methods, privacy has become highly critical. As a result, various privacy-preserving data analysis technologies have emerged. Hence, we use the randomization process to reconstruct composite data attributes accurately. Also, we use privacy measures to estimate how much deception is required to guarantee privacy. There are several viable privacy protections; however, determining which one is the best is still a work in progress. This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results. Furthermore, this paper investigates the use of arbitrary nature with perturbations in privacy preservation. According to the research, arbitrary objects (most notably random matrices) have "predicted" frequency patterns. It shows how to recover crucial information from a sample damaged by a random number using an arbitrary lattice spectral selection strategy. This filtration system's conceptual framework posits, and extensive practical findings indicate that sparse data distortions preserve relatively modest privacy protection in various situations. As a result, the research framework is efficient and effective in maintaining data privacy and security.

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“…In [8], they used the randomness approach to distort data. The probability density function is reliant on this strategy.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Murugeshwari¹,

Rajalakshmi²,

Sudharson³

2023

Computer Systems Science and Engineering

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“…CBM will have a piece of data possessor's enciphered information in light of the above pre-processing models. Henceforth, CBM has no idea about the information about data possessors [24][25][26]. Two protocols combine all CBM block results and determine the global block result.…”

Section: Security Under the Third-party / Cbm Attacksmentioning

confidence: 99%

“…However, due to classical encryption, the mining process does not affect. Each data possessor obtains the encrypted frequent itemset and the association rule for the assigned block using the apriori algorithm's standard calculation [22][23][24].…”

Section: Protocol-b: Computation Based On Data Possessormentioning

confidence: 99%

Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

Dhinakaran¹,

Prathap²

2022

Intelligent Automation &Amp; Soft Computing

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These days, investigations of information are becoming essential for various associations all over the globe. By and large, different associations need to perform information examinations on their joined data sets. Privacy and security have become a relentless concern wherein business experts do not desire to contribute their classified transaction data. Therefore, there is a requirement to build a proficient methodology that can process the broad mixture of data and convert those data into meaningful knowledge to the user without forfeiting the security and privacy of individuals' crude information. We devised two unique protocols for frequent mining itemsets in horizontally partitioned datasets while maintaining privacy. In such a scenario, data possessors outwork mining tasks on their multiparty data by preserving privacy. The proposed framework model encompasses two or more data possessors who encrypt their information and dispense their encrypted data to two or more clouds by a data share allocator algorithm. This methodology protects the data possessor's raw data from other data possessors and the other clouds. To guarantee data privacy, we plan a proficient enhanced homomorphic encryption conspire. Our approach ensures privacy during communication and accumulation of data and guarantees no information or data adversity and no incidental consequences for data utility. Therefore, the advantages of data mining have remained redesigned. To approve the exhibition of our protocols, we implemented the protocols through broad experiments, where the assessment outcome showed that the mined results obtained by our protocols are reliable to those obtained by a traditional sole-machine approach. Meanwhile, the findings of our performance assessment have shown that our methodology is very efficient, with reasonably reduced communication time and computation costs.

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“…The single application of noise was also performed by some researchers [110,111,183]. The experimental studies allow us to conclude that the level of noise does affect the classification error.…”

Section: Impact On Predictive Performancementioning

confidence: 99%

Survey on Privacy-Preserving Techniques for Data Publishing

Carvalho¹,

Moniz²,

Faria³

et al. 2022

Preprint

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The exponential growth of collected, processed, and shared microdata has given rise to concerns about individuals' privacy. As a result, laws and regulations have emerged to control what organisations do with microdata and how they protect it. Statistical Disclosure Control seeks to reduce the risk of confidential information disclosure by de-identifying them. Such de-identification is guaranteed through privacy-preserving techniques. However, de-identified data usually results in loss of information, with a possible impact on data analysis precision and model predictive performance. The main goal is to protect the individuals' privacy while maintaining the interpretability of the data, i.e. its usefulness. Statistical Disclosure Control is an area that is expanding and needs to be explored since there is still no solution that guarantees optimal privacy and utility. This survey focuses on all steps of the de-identification process. We present existing privacy-preserving techniques used in microdata de-identification, privacy measures suitable for several disclosure types and, information loss and predictive performance measures. In this survey, we discuss the main challenges raised by privacy constraints, describe the main approaches to handle these obstacles, review taxonomies of privacy-preserving techniques, provide a theoretical analysis of existing comparative studies, and raise multiple open issues.

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Privacy preserving classification over differentially private data

Cited by 8 publications

References 46 publications

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm

Survey on Privacy-Preserving Techniques for Data Publishing

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