Log in

Relevant bibliographies by topics / And Data Privacy / Journal articles

To see the other types of publications on this topic, follow the link: And Data Privacy.

Journal articles on the topic 'And Data Privacy'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'And Data Privacy.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Gade, Kishore. "Data Analytics: Data Privacy, Data Ethics, Data Monetization." International Journal of Science and Research (IJSR) 9, no. 2 (2020): 1953–59. http://dx.doi.org/10.21275/sr20027110931.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Yerbulatov, Sultan. "Data Security and Privacy in Data Engineering." International Journal of Science and Research (IJSR) 13, no. 4 (2024): 232–36. http://dx.doi.org/10.21275/es24318121241.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

R.Mahesh and Dr.T.Meyyappan. "A New Method for Preserving Privacy in Data Publishing Against Attribute and Identity Disclosure Risk." International Journal on Cryptography and Information Security (IJCIS) 3, no. 2 (2020): 23–30. https://doi.org/10.5281/zenodo.4013312.

Full text

Abstract:

Sharing, transferring, mining and publishing data are fundamental operations in day to day life. Preserving the privacy of individuals is essential one. Sensitive personal information must be protected when data are published. There are two kinds of risks namely attribute disclosure and identity disclosure that affects privacy of individuals whose data are published. Early Researchers have contributed new methods namely k-anonymity, l-diversity, t-closeness to preserve privacy. K-anonymity method preserves privacy of individuals against identity disclosure attack alone. But Attribute disclosur

APA, Harvard, Vancouver, ISO, and other styles

4

P, Ram Mohan Rao, Murali Krishna S, and P. Siva Kumar A. "Novel algorithm for efficient privacy preservation in data analytics." Indian Journal of Science and Technology 14, no. 6 (2021): 519–26. https://doi.org/10.17485/IJST/v14i6.1773.

Full text

Abstract:

Abstract <strong>Objective:</strong> To address the modern privacy threats in data analytics by designing an efficient privacy preserving data analytics technique.<strong> Methods:</strong> The method applied is a non anonymized method that uses the concepts of synthesizing quasi identifiers and application of differential privacy. The proposed method was applied to three data sets viz. Adult data set, Statlogdata set and Indian Liver Patient data set. All the data sets are freely available in the UCI repository. Findings: The study presents “Synthesize Quasi Identifiers a

APA, Harvard, Vancouver, ISO, and other styles

5

Torra, Vicenç, and Guillermo Navarro-Arribas. "Data privacy." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4, no. 4 (2014): 269–80. http://dx.doi.org/10.1002/widm.1129.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Hassan, Jamal, A. Algeelani Nasir, and Al-Sammarraie Najeeb. "Advance Data-Privacy by Using Artificial Intelligence." International Journal of Computer Science and Information Technology Research 10, no. 4 (2022): 38–45. https://doi.org/10.5281/zenodo.7398762.

Full text

Abstract:

<strong>Abstract:</strong> With the progression utilize of computers broadly, utilization of the information has moreover developed to a huge level. Nowadays information is collected without any reason, and each action of a machine or a human being is recorded, On the off chance that required in the future, at that point, the information will be dissected but here the address of believe emerges as the information will go through numerous stages for the investigation by distinctive parties. The information may contain a few touchy or private data which can be mutualized by the organizations inc

APA, Harvard, Vancouver, ISO, and other styles

7

Basha, M. John, T. Satyanarayana Murthy, A. S. Valarmathy, et al. "Privacy-Preserving Data Mining and Analytics in Big Data." E3S Web of Conferences 399 (2023): 04033. http://dx.doi.org/10.1051/e3sconf/202339904033.

Full text

Abstract:

Privacy concerns have gotten more attention as Big Data has spread. The difficulties of striking a balance between the value of data and individual privacy have led to the emergence of privacy-preserving data mining and analytics approaches as a crucial area of research. An overview of the major ideas, methods, and developments in privacy-preserving data mining and analytics in the context of Big Data is given in this abstract. Data mining that protects privacy tries to glean useful insights from huge databases while shielding the private data of individuals. Commonly used in traditional data

APA, Harvard, Vancouver, ISO, and other styles

8

Krishna, Prasanth Brahmaji Kanagarla. "The Role of Synthetic Data in Ensuring Data Privacy and Enabling Secure Analytics." European Journal of Advances in Engineering and Technology 11, no. 10 (2024): 75–79. https://doi.org/10.5281/zenodo.14012320.

Full text

Abstract:

This research focuses on the analysis of the role synthetic data could play in the maintenance of privacy under the GDPR regulations. It considers, for actual effectiveness, some of the methods for generating synthetic data, like differential privacy and GANs. The study also shows some challenges to organizations about compliance with the data, since the main use of synthetic data is analytical. The performance of the experiments revealed that synthetic data represented a good balance regarding the protection of privacy. This presented the assumption that new methods are, above all, the key to

APA, Harvard, Vancouver, ISO, and other styles

9

Mrs., M. S. Lakshmi Devi. "Privacy-Preserving Data Fragmentation and Aggregation." Journal of Scholastic Engineering Science and Management 2, no. 9 (2023): 23–30. https://doi.org/10.5281/zenodo.8311026.

Full text

Abstract:

<strong>Privacy-preserving data fragmentation and aggregation techniques aim to preserve the privacy of individual data points while still allowing for the aggregation of data for analysis. This is important for applications such as medical research, where it is necessary to share data without compromising the privacy of the patients. This paper surveys the state-of-the-art in privacy-preserving data fragmentation and aggregation techniques. We discuss the different challenges that need to be addressed in this area, and we present a number of different techniques that have been proposed. We al

APA, Harvard, Vancouver, ISO, and other styles

10

COSTEA, Ioan. "Data Privacy Assurance in Virtual Private Networks." International Journal of Information Security and Cybercrime 1, no. 2 (2012): 40–47. http://dx.doi.org/10.19107/ijisc.2012.02.05.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Sramka, Michal. "Data mining as a tool in privacy-preserving data publishing." Tatra Mountains Mathematical Publications 45, no. 1 (2010): 151–59. http://dx.doi.org/10.2478/v10127-010-0011-z.

Full text

Abstract:

ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and d

APA, Harvard, Vancouver, ISO, and other styles

12

Mohapatra, Shubhankar, Jianqiao Zong, Florian Kerschbaum, and Xi He. "Differentially Private Data Generation with Missing Data." Proceedings of the VLDB Endowment 17, no. 8 (2024): 2022–35. http://dx.doi.org/10.14778/3659437.3659455.

Full text

Abstract:

Despite several works that succeed in generating synthetic data with differential privacy (DP) guarantees, they are inadequate for generating high-quality synthetic data when the input data has missing values. In this work, we formalize the problems of DP synthetic data with missing values and propose three effective adaptive strategies that significantly improve the utility of the synthetic data on four real-world datasets with different types and levels of missing data and privacy requirements. We also identify the relationship between privacy impact for the complete ground truth data and in

APA, Harvard, Vancouver, ISO, and other styles

13

M., Santhiya Devi*1 &. Dr.K.Arunesh2. "PRIVACY PRESERVATION TECHNIQUES FOR PERSONALIZED DATA IN BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 4 (2019): 335–40. https://doi.org/10.5281/zenodo.2653603.

Full text

Abstract:

The recent advancements in this digital world huge amount of information are generated and shared, and the management of such large data is the most difficult and challenging task. Due to its size and variety of data, its name big data was derived. In the management of this data, some information may be disclosed. This type of disclosure can lead to leakage of Personal Identifiable Information (PII), as it contains individual’s information. The voluminous data generated from the various sources can be processed and analyzed to support decision making. However, data analytics is prone to

APA, Harvard, Vancouver, ISO, and other styles

14

Qamar, T., N. Z. Bawany, and N. A. Khan. "EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing." Engineering, Technology & Applied Science Research 10, no. 2 (2020): 5423–27. https://doi.org/10.5281/zenodo.3748328.

Full text

Abstract:

The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of a person’s private information represents a serious threat to his/her privacy. Privacy-Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a ch

APA, Harvard, Vancouver, ISO, and other styles

15

Heubl, B. "News - Briefing. Data privacy: Data privacy group found to have breached online privacy rules." Engineering & Technology 15, no. 3 (2020): 9. http://dx.doi.org/10.1049/et.2020.0317.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Winarsih, Winarsih, and Irwansyah Irwansyah. "PROTEKSI PRIVASI BIG DATA DALAM MEDIA SOSIAL." Jurnal Audience 3, no. 1 (2020): 1–33. http://dx.doi.org/10.33633/ja.v3i1.3722.

Full text

Abstract:

AbstrakPerkembangan media sosial di Indonesia begitu pesat dengan jumlah pengguna yang terus meningkat. Akan tetapi hal tersebut kurang diimbangi dengan kesadaran tentang privasi dalam kaitannya dengan big data yang dihasilkan oleh penyedia layanan. Penyedia layanan memberikan kebijakan berupa syarat dan ketentuan akan tetapi masyarakat umumnya masih rendah dalam hal memiliki kesadaran tentang privasi data pribadi mereka. Penelitian ini bertujuan untuk mengetahui solusi dari permasalahan privasi big data dalam media sosial dan dianalisis dengan teori privasi komunikasi. Metode yang digunakan d

APA, Harvard, Vancouver, ISO, and other styles

17

JAKŠIĆ, SVETLANA, JOVANKA PANTOVIĆ, and SILVIA GHILEZAN. "Linked data privacy." Mathematical Structures in Computer Science 27, no. 1 (2015): 33–53. http://dx.doi.org/10.1017/s096012951500002x.

Full text

Abstract:

Web of Linked Data introduces common format and principles for publishing and linking data on the Web. Such a network of linked data is publicly available and easily consumable. This paper introduces a calculus for modelling networks of linked data with encoded privacy preferences.In that calculus, a network is a parallel composition of users, where each user is named and consists of data, representing the user's profile, and a process. Data is a parallel composition of triples with names (resources) as components. Associated with each name and each triple of names are their privacy protection

APA, Harvard, Vancouver, ISO, and other styles

18

Abdul Manap, Nazura, Mohamad Rizal Abd Rahman, and Siti Nur Farah Atiqah Salleh. "HEALTH DATA OWNERSHIP IN MALAYSIA PUBLIC AND PRIVATE HEALTHCARE: A LEGAL ANALYSIS OF HEALTH DATA PRIVACY IN THE AGE OF BIG DATA." International Journal of Law, Government and Communication 7, no. 30 (2022): 33–41. http://dx.doi.org/10.35631/ijlgc.730004.

Full text

Abstract:

Health data ownership in big data is a new legal issue. The problem stands between the public and private healthcare as the main proprietor of health data. In Malaysia, health data ownership is under government hospitals and private healthcare jurisdictions. Who owns the data will be responsible for safeguarding it, including its privacy. Various technical methods are applied to protect health data, such as aggregation and anonymization. The thing is, do these technical methods are still reliable to safeguard privacy in big data? In terms of legal protection, private healthcare is governed und

APA, Harvard, Vancouver, ISO, and other styles

19

DE CAPITANI DI VIMERCATI, SABRINA, SARA FORESTI, GIOVANNI LIVRAGA, and PIERANGELA SAMARATI. "DATA PRIVACY: DEFINITIONS AND TECHNIQUES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 06 (2012): 793–817. http://dx.doi.org/10.1142/s0218488512400247.

Full text

Abstract:

The proper protection of data privacy is a complex task that requires a careful analysis of what actually has to be kept private. Several definitions of privacy have been proposed over the years, from traditional syntactic privacy definitions, which capture the protection degree enjoyed by data respondents with a numerical value, to more recent semantic privacy definitions, which take into consideration the mechanism chosen for releasing the data. In this paper, we illustrate the evolution of the definitions of privacy, and we survey some data protection techniques devised for enforcing such d

APA, Harvard, Vancouver, ISO, and other styles

20

Bhoomi, Shukla. "DATA PRIVACY, DATA PROTECTION: "The Unprecedented Challenges of Ambient Intelligence"." Indian Journal of Law and Society I, no. 8 (2024): 25–31. https://doi.org/10.5281/zenodo.10644515.

Full text

Abstract:

<strong>ABSTRACT</strong> <em>Privacy has emerged as a basic human right across the globe and in India too it has been recognized as a Fundamental Right under Article 21 of the Indian Constitution. Right to Privacy is closely related to the protection of data which in this technological and globalized world, has become very difficult to achieve. Further, violation of privacy rights by the ruling majority through discriminatory legislation has also become possible due to lack of legal protection to this Right. In India, this Right was not initially recognized as a Fundamental Right, neither any

APA, Harvard, Vancouver, ISO, and other styles

21

Smith, J. H., and JS Horne. "Data privacy and DNA data." IASSIST Quarterly 47, no. 3-4 (2023): 1–3. http://dx.doi.org/10.29173/iq1094.

Full text

Abstract:

The letter to the Editor is in response to the manuscript by Hertzog et al. (2023) titled "Data management instruments to Protect the personal information of Children and Adolescents in sub-Saharan Africa." The letter elaborates on personal data protection, particularly the POPI Act's data management requirements; the DNA Act mandates specific measures to ensure the data integrity and security of the NFDD's information. In addition, it criminalises the misuse or compromise of the data's integrity within the NFDD. In addition, the DNA Act established the National Forensic Oversight and Ethical

APA, Harvard, Vancouver, ISO, and other styles

22

Ravindar, Reddy Gopireddy. "Data Anonymization Techniques: Ensuring Privacy in Big Data Analytics." European Journal of Advances in Engineering and Technology 7, no. 11 (2020): 68–74. https://doi.org/10.5281/zenodo.13253009.

Full text

Abstract:

Today in the age of big data, a huge amount information is gathered, refined and organized so these are easy to analyzed furnishing proper insight into it. But this is a major privacy concern because all your personal and sensitive data could potentially be accessed by someone else to do some wrong. Anonymization of data is important to balance the right on privacy when considering big-data use-cases. This paper evaluates different data anonymization techniques, their effectiveness and challenges in big data analytics with privacy. It also talks about the trade-off between usability and privac

APA, Harvard, Vancouver, ISO, and other styles

23

Patel, Mohnish, Prashant Richariya, and Anurag Shrivastava. "A review paper on Privacy-Preserving Data Mining." COMPUSOFT: An International Journal of Advanced Computer Technology 02, no. 09 (2013): 296–99. https://doi.org/10.5281/zenodo.14613361.

Full text

Abstract:

Data mining technology help us in extraction of useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some private information about individuals, businesses and organizations has to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important issue in current years. This paper we propose an evolutionary privacy-preserving data mining technology to find appropriate method to perform secure transactions into a database 

APA, Harvard, Vancouver, ISO, and other styles

24

Qamar, T., N. Z. Bawany, and N. A. Khan. "EDAMS: Efficient Data Anonymization Model Selector for Privacy-Preserving Data Publishing." Engineering, Technology & Applied Science Research 10, no. 2 (2020): 5423–27. http://dx.doi.org/10.48084/etasr.3374.

Full text

Abstract:

The evolution of internet to the Internet of Things (IoT) gives an exponential rise to the data collection process. This drastic increase in the collection of a person’s private information represents a serious threat to his/her privacy. Privacy-Preserving Data Publishing (PPDP) is an area that provides a way of sharing data in their anonymized version, i.e. keeping the identity of a person undisclosed. Various anonymization models are available in the area of PPDP that guard privacy against numerous attacks. However, selecting the optimum model which balances utility and privacy is a challeng

APA, Harvard, Vancouver, ISO, and other styles

25

Lobo-Vesga, Elisabet, Alejandro Russo, and Marco Gaboardi. "A Programming Language for Data Privacy with Accuracy Estimations." ACM Transactions on Programming Languages and Systems 43, no. 2 (2021): 1–42. http://dx.doi.org/10.1145/3452096.

Full text

Abstract:

Differential privacy offers a formal framework for reasoning about the privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analysis results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages to ease the implementation of differentially

APA, Harvard, Vancouver, ISO, and other styles

26

Luo, Xiaohui. "A Method for Privacy-Safe Synthetic Health Data." Academic Journal of Science and Technology 10, no. 1 (2024): 445–50. http://dx.doi.org/10.54097/f7fjss40.

Full text

Abstract:

Private health records are important for medical research but hard to get because of legal rules. This shortage of data can be solved by using generative models like GANs, which make new, similar data. But GANs might leak private information. To fix this, we made a new kind of GAN with a privacy protection part called DP-ACTGAN. It uses differential privacy to keep the original data safe. We also put a classifier in the GAN to make sure the new data is very close to the real data. Experiments show that DP-ACTGAN can make good quality data without giving away private information. This means we

APA, Harvard, Vancouver, ISO, and other styles

27

Gertner, Yael, Yuval Ishai, Eyal Kushilevitz, and Tal Malkin. "Protecting Data Privacy in Private Information Retrieval Schemes." Journal of Computer and System Sciences 60, no. 3 (2000): 592–629. http://dx.doi.org/10.1006/jcss.1999.1689.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Yang, Qing, Cheng Wang, Teng Hu, Xue Chen, and Changjun Jiang. "Implicit privacy preservation: a framework based on data generation." Security and Safety 1 (2022): 2022008. http://dx.doi.org/10.1051/sands/2022008.

Full text

Abstract:

This paper addresses a special and imperceptible class of privacy, called implicit privacy. In contrast to traditional (explicit) privacy, implicit privacy has two essential properties: (1) It is not initially defined as a privacy attribute; (2) it is strongly associated with privacy attributes. In other words, attackers could utilize it to infer privacy attributes with a certain probability, indirectly resulting in the disclosure of private information. To deal with the implicit privacy disclosure problem, we give a measurable definition of implicit privacy, and propose an ex-ante implicit pr

APA, Harvard, Vancouver, ISO, and other styles

29

Oyekan, Basirat. "DEVELOPING PRIVACY-PRESERVING FEDERATED LEARNING MODELS FOR COLLABORATIVE HEALTH DATA ANALYSIS ACROSS MULTIPLE INSTITUTIONS WITHOUT COMPROMISING DATA SECURITY." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 3, no. 3 (2024): 139–64. http://dx.doi.org/10.60087/jklst.vol3.n3.p139-164.

Full text

Abstract:

Federated learning is an emerging distributed machine learning technique that enables collaborative training of models among devices and servers without exchanging private data. However, several privacy and security risks associated with federated learning need to be addressed for safe adoption. This review provides a comprehensive analysis of the key threats in federated learning and the mitigation strategies used to overcome these threats. Some of the major threats identified include model inversion, membership inference, data attribute inference and model extraction attacks. Model inversion

APA, Harvard, Vancouver, ISO, and other styles

30

Gayathri, Tata, and N. Durga. "Privacy Preserving Approaches for High Dimensional Data." International Journal of Trend in Scientific Research and Development Volume-1, Issue-5 (2017): 1120–25. http://dx.doi.org/10.31142/ijtsrd2430.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Sandeep Agrawal, Tanay. "Blockchain Applications in Data Security and Privacy." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 22–23. https://doi.org/10.21275/sr241127154835.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

M, Madhushree. "Privacy Preserving Utility Verification of Data Published." International Journal of Science and Research (IJSR) 10, no. 8 (2021): 1126–29. https://doi.org/10.21275/sr21825190409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Purnamaningsih, Sari Nur Indahty, Joko Ismono, Ichwani Siti Utami, Vernando Parlindungan, and Salma Nur Hanifah. "The Challenges of Data Privacy Laws in the Age of Big Data: Balancing Security, Privacy, and Innovation." Join: Journal of Social Science 1, no. 6 (2024): 455–65. http://dx.doi.org/10.59613/gny8bq82.

Full text

Abstract:

This study explores the complexities and challenges of implementing data privacy laws in the era of big data, where security, privacy, and innovation frequently intersect. The exponential growth of data collection, driven by advancements in technology and the widespread adoption of digital services, has intensified the need for effective data privacy regulations. However, balancing the protection of individual privacy with the demands of innovation and security presents considerable challenges for policymakers. Utilizing a qualitative approach, this study employs a literature review and librar

APA, Harvard, Vancouver, ISO, and other styles

34

Riyana, Surapon, Nobutaka Ito, Tatsanee Chaiya, et al. "Privacy Threats and Privacy Preservation Techniques for Farmer Data Collections Based on Data Shuffling." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (2022): 289–301. http://dx.doi.org/10.37936/ecti-cit.2022163.246469.

Full text

Abstract:

Aside from smart technologies, farm data collection is also important for smart farms including farm environment data collection and farmer survey data collection. With farm data collection, we observe that it is generally proposed to utilize in smart farm systems. However, it can also be released for use in the outside scope of the data collecting organization for an appropriate business reason such as improving the smart farm system, product quality, and customer service. Moreover, we can observe that the farmer survey data collection often includes sensitive data, the private data of farmer

APA, Harvard, Vancouver, ISO, and other styles

35

Zhu, Xiao Ming. "Research on Privacy Preserving Data Mining Association Rules Protocol." Advanced Materials Research 756-759 (September 2013): 1661–64. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1661.

Full text

Abstract:

Privacy preserving in data mining is a significant direction. There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product. Then, the data mining and secure multiparty computation are briefly introduced. And proposes an implementation for privacy preserving mining protocol based secure multiparty c

APA, Harvard, Vancouver, ISO, and other styles

36

Jain, Pinkal, Vikas Thada, and Deepak Motwani. "Providing Highest Privacy Preservation Scenario for Achieving Privacy in Confidential Data." International Journal of Experimental Research and Review 39, Spl Volume (2024): 190–99. http://dx.doi.org/10.52756/ijerr.2024.v39spl.015.

Full text

Abstract:

Machine learning algorithms have been extensively employed in multiple domains, presenting an opportunity to enable privacy. However, their effectiveness is dependent on enormous data volumes and high computational resources, usually available online. It entails personal and private data like mobile telephone numbers, identification numbers, and medical histories. Developing efficient and economical techniques to protect this private data is critical. In this context, the current research suggests a novel way to accomplish this, combining modified differential privacy with a more complicated m

APA, Harvard, Vancouver, ISO, and other styles

37

Raj, Diana Judith Irudaya, Vijay Sai Radhakrishnan, Manyam Rajasekhar Reddy, Natarajan Senthil Selvan, Balasubramanian Elangovan, and Manikandan Ganesan. "The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining." Engineering, Technology & Applied Science Research 14, no. 4 (2024): 15969–74. http://dx.doi.org/10.48084/etasr.7969.

Full text

Abstract:

Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a bal

APA, Harvard, Vancouver, ISO, and other styles

38

Duan, Huabin, Jie Yang, and Huanjun Yang. "A Blockchain-Based Privacy Protection Application for Logistics Big Data." Journal of Cases on Information Technology 24, no. 5 (2022): 1–12. http://dx.doi.org/10.4018/jcit.295249.

Full text

Abstract:

Logistics business is generally managed by logistics orders in plain text, and there is a risk of disclosure of customer privacy information in every business link. In order to solve the problem of privacy protection in logistics big data system, a new kind of logistics user privacy data protection scheme is proposed. First of all, an access rights management mechanism is designed by combining block chain and anonymous authentication to realize the control and management of users' access rights to private data. Then, the privacy and confidentiality protection between different services is real

APA, Harvard, Vancouver, ISO, and other styles

39

Appenzeller, Arno, Moritz Leitner, Patrick Philipp, Erik Krempel, and Jürgen Beyerer. "Privacy and Utility of Private Synthetic Data for Medical Data Analyses." Applied Sciences 12, no. 23 (2022): 12320. http://dx.doi.org/10.3390/app122312320.

Full text

Abstract:

The increasing availability and use of sensitive personal data raises a set of issues regarding the privacy of the individuals behind the data. These concerns become even more important when health data are processed, as are considered sensitive (according to most global regulations). PETs attempt to protect the privacy of individuals whilst preserving the utility of data. One of the most popular technologies recently is DP, which was used for the 2020 U.S. Census. Another trend is to combine synthetic data generators with DP to create so-called private synthetic data generators. The objective

APA, Harvard, Vancouver, ISO, and other styles

40

Du, Jiawen, and Yong Pi. "Research on Privacy Protection Technology of Mobile Social Network Based on Data Mining under Big Data." Security and Communication Networks 2022 (January 13, 2022): 1–9. http://dx.doi.org/10.1155/2022/3826126.

Full text

Abstract:

With the advent of the era of big data, people’s lives have undergone earth-shaking changes, not only getting rid of the cumbersome traditional data collection but also collecting and sorting information directly from people’s footprints on social networks. This paper explores and analyzes the privacy issues in current social networks and puts forward the protection strategies of users’ privacy data based on data mining algorithms so as to truly ensure that users’ privacy in social networks will not be illegally infringed in the era of big data. The data mining algorithm proposed in this paper

APA, Harvard, Vancouver, ISO, and other styles

41

Varun, Pratap Singh Bhakuni, and Chandr Singh Nalnish. "Digital Data vis a vis Right to Privacy in India." Recent Researches in Social Sciences & Humanties 11, no. 1 (Jan.-Feb.-Mar. 2024) (2024): 87–92. https://doi.org/10.5281/zenodo.11001650.

Full text

Abstract:

In the digital age, where personal data has become increasingly valuable and ubiquitous, the right toprivacy has emerged as a critical concern. This research article explores the concept of digital dataprivacy in India, examining its legal framework, recent developments, and challenges. It exploresimportant laws like the Sensitive Personal Data or Information) Rules, 2011 and the InformationTechnology (Reasonable Security Practices and Procedures) Rules, 2011, as well as the Supreme Court'sseminal ruling in Justice K.S. Puttaswamy (Retd.) v. Union of India, which upheld the fundamentalright to

APA, Harvard, Vancouver, ISO, and other styles

42

Arjun, Mantri. "Ensuring Data Security and Privacy During Data Migration." European Journal of Advances in Engineering and Technology 6, no. 3 (2019): 111–15. https://doi.org/10.5281/zenodo.13354011.

Full text

Abstract:

Data migration is a critical process for transferring data between different storage systems, formats, or computing environments, often driven by technological upgrades, cloud adoption, and organizational restructuring. Ensuring data security and privacy during this process is paramount to prevent data breaches and comply with regulatory requirements. This paper discusses comprehensive strategies, including encryption, access control, adherence to data protection regulations, and effective data governance, to mitigate risks associated with data migration. By implementing these techniques, orga

APA, Harvard, Vancouver, ISO, and other styles

43

Mantsha, Arif, and kumar Dheeraj. "Privacy and Data Protection : A Critical Analysis in Context of Existing Data Protection Laws." Career Point International Journal of Research(CPIJR) 2, no. 2 (2023): 44–49. https://doi.org/10.5281/zenodo.8312345.

Full text

Abstract:

<em>The right to life and individual freedom are guaranteed by Article 21 of the Indian Constitution. The aforementioned fundamental right includes the right to privacy as a key component. Privacy, which is frequently mistaken with trade secrets and confidentiality and only applies to information specific to individuals, refers to the use and disclosure of personal information. In India, there isn't currently any explicit regulation covering data protection and privacy. Different laws relating to information technology, intellectual property laws, crimes, and commercial relationships can b

APA, Harvard, Vancouver, ISO, and other styles

44

BRYZHKO, V. "Data privacy in cloud technologies." INFORMATION AND LAW, no. 4(19) (December 15, 2016): 47–59. http://dx.doi.org/10.37750/2616-6798.2016.4(19).272976.

Full text

Abstract:

On the privacy and personal data protection in terms of modern technologies development. Suggestions are provided in relation to introduction of institute of right of private ownership of a person on the personal information in Ukraine.

APA, Harvard, Vancouver, ISO, and other styles

45

Vishnoi, Meenakshi, and Seeja K. R. "Privacy Preserving Data Mining using Attribute Encryption and Data Perturbation." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 6, no. 3 (2013): 370–78. http://dx.doi.org/10.24297/ijct.v6i3.4461.

Full text

Abstract:

Data mining is a very active research area that deals with the extraction of Â knowledge from very large databases. Data mining has made knowledge extraction and decision making easy. The extracted knowledge could reveal the personal information , if the data contains various private and sensitive attributes about an individual. This poses a threat to the personal information as there is a possibility of misusing the information behind the scenes without the knowledge of the individual. So, privacy becomes a great concern for the data owners and the organizations Â as none of the organizations

APA, Harvard, Vancouver, ISO, and other styles

46

Silva, Paulo, Carolina Gonçalves, Nuno Antunes, Marilia Curado, and Bogdan Walek. "Privacy risk assessment and privacy-preserving data monitoring." Expert Systems with Applications 200 (August 2022): 116867. http://dx.doi.org/10.1016/j.eswa.2022.116867.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Yao-Huai, Lü. "Privacy and Data Privacy Issues in Contemporary China." Ethics and Information Technology 7, no. 1 (2005): 7–15. http://dx.doi.org/10.1007/s10676-005-0456-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Ingale, Indrajeet. "Privacy Preserving of Collaborative Data Publishing WithM-Privacy." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 2 (2015): 845–47. http://dx.doi.org/10.17762/ijritcc2321-8169.150290.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Wang, Yi-Ren, and Yun-Cheng Tsai. "The Protection of Data Sharing for Privacy in Financial Vision." Applied Sciences 12, no. 15 (2022): 7408. http://dx.doi.org/10.3390/app12157408.

Full text

Abstract:

The primary motivation is to address difficulties in data interpretation or a reduction in model accuracy. Although differential privacy can provide data privacy guarantees, it also creates problems. Thus, we need to consider the noise setting for differential privacy is currently inconclusive. This paper’s main contribution is finding a balance between privacy and accuracy. The training data of deep learning models may contain private or sensitive corporate information. These may be dangerous to attacks, leading to privacy data leakage for data sharing. Many strategies are for privacy protect

APA, Harvard, Vancouver, ISO, and other styles

50

David, Shamoo Excel. "Privacy-preserving data analysis." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 597–609. https://doi.org/10.5281/zenodo.14930698.

Full text

Abstract:

With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniques—Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscation—to discern their efficacy and trade-offs in terms of data utility

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!