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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
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Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
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Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 150-158 |
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Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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URL |
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Title |
Privacy-Preserving in the Context of Data Mining and Deep Learning |
|
Author |
Amjaad Altalhi, Maram AL-Saedi, Hatim Alsuwat, and Emad Alsuwat |
| Citation |
Vol. 21 No. 6 pp. 137-142 |
|
Abstract |
Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons. |
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Keywords |
Privacy-Preserving Machine Learning; Data Mining; Deep Learning. |
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