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Journal articles on the topic 'Text privacy'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Kanyar, Mohammad Naeem. "Differential Privacy “Working Towards Differential Privacy for Sensitive Text “." International Journal of Engineering and Computer Science 12, no. 04 (2023): 25691–99. http://dx.doi.org/10.18535/ijecs/v12i04.4727.

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The differential-privacy idea states that maintaining privacy often includes adding noise to a data set to make it more challenging to identify data that corresponds to specific individuals. The accuracy of data analysis is typically decreased when noise is added, and differential privacy provides a technique to evaluate the accuracy-privacy trade-off. Although it may be more difficult to discern between analyses performed on somewhat dissimilar data sets, injecting random noise can also reduce the usefulness of the analysis. If not, enough noise is supplied to a very tiny data collection, ana

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Shree, A. N. Ramya, and Kiran P. "Privacy Preserving Text Document Summarization." Journal of Engineering Research and Sciences 1, no. 7 (2022): 7–14. http://dx.doi.org/10.55708/js0107002.

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Dopierała, Renata. "Społeczne wyobrażenia prywatności." Kultura i Społeczeństwo 50, no. 1-2 (2006): 307–19. http://dx.doi.org/10.35757/kis.2006.50.1-2.14.

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In the paper author considers social representations of privacy based on empirical research — The Unfinished Sentences Test. The main goals of the text are to begin defining such terms as: private life and private sphere, and to discuss functions of privacy, how it is experienced and its role in individual and social life. It also deals with the threats to privacy in contemporary societies.

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Bihani, Geetanjali. "Interpretable Privacy Preservation of Text Representations Using Vector Steganography." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12872–73. http://dx.doi.org/10.1609/aaai.v36i11.21573.

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Contextual word representations generated by language models learn spurious associations present in the training corpora. Adversaries can exploit these associations to reverse-engineer the private attributes of entities mentioned in the training corpora. These findings have led to efforts towards minimizing the privacy risks of language models. However, existing approaches lack interpretability, compromise on data utility and fail to provide privacy guarantees. Thus, the goal of my doctoral research is to develop interpretable approaches towards privacy preservation of text representations tha

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Liang, Zi, Pinghui Wang, Ruofei Zhang, et al. "MERGE: Fast Private Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 18 (2024): 19884–92. http://dx.doi.org/10.1609/aaai.v38i18.29964.

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The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language generation (NLG), crucial for applications like translation and code completion, remains underexplored. In addition, previous privacy-preserving techniques suffer from convergence issues during model training and exhibit poor inference spee

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Wunderlich, Dominik, Daniel Bernau, Francesco Aldà, Javier Parra-Arnau, and Thorsten Strufe. "On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification." Applied Sciences 12, no. 21 (2022): 11177. http://dx.doi.org/10.3390/app122111177.

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Hierarchical text classification consists of classifying text documents into a hierarchy of classes and sub-classes. Although Artificial Neural Networks have proved useful to perform this task, unfortunately, they can leak training data information to adversaries due to training data memorization. Using differential privacy during model training can mitigate leakage attacks against trained models, enabling the models to be shared safely at the cost of reduced model accuracy. This work investigates the privacy–utility trade-off in hierarchical text classification with differential privacy guara

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Pang, Hweehwa, Jialie Shen, and Ramayya Krishnan. "Privacy-preserving similarity-based text retrieval." ACM Transactions on Internet Technology 10, no. 1 (2010): 1–39. http://dx.doi.org/10.1145/1667067.1667071.

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Vadlapati, Praneeth. "TokEncryption: Enhanced Hashing of Text using Tokenization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem20280.

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In the current digital space, the security of sensitive information, such as passwords and private data, is of high importance. Traditional hashing methods might not adequately address data privacy concerns or vulnerabilities created due to weak passwords. Tokenization methods are utilized in natural language processing (NLP). This paper introduces a method called “TokEncryption” to utilize tokens for one-way encryption of text called hashing. A tokenizer is used to generate tokens for an input text, which are utilized to encrypt the text to create secure encrypted text. Different characters o

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Zhu, You-wen, Liu-sheng Huang, Dong Li, and Wei Yang. "Privacy-preserving Text Information Hiding Detecting Algorithm." Journal of Electronics & Information Technology 33, no. 2 (2011): 278–83. http://dx.doi.org/10.3724/sp.j.1146.2010.00375.

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Tejaswini, G. "Cipher Text Policy Privacy Attribute-Based Security." International Journal of Reliable Information and Assurance 5, no. 1 (2017): 15–20. http://dx.doi.org/10.21742/ijria.2017.5.1.03.

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Xiong, Xingxing, Shubo Liu, Dan Li, Jun Wang, and Xiaoguang Niu. "Locally differentially private continuous location sharing with randomized response." International Journal of Distributed Sensor Networks 15, no. 8 (2019): 155014771987037. http://dx.doi.org/10.1177/1550147719870379.

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With the growing popularity of fifth-generation-enabled Internet of Things devices with localization capabilities, as well as on-building fifth-generation mobile network, location privacy has been giving rise to more frequent and extensive privacy concerns. To continuously enjoy services of location-based applications, one needs to share his or her location information to the corresponding service providers. However, these continuously shared location information will give rise to significant privacy issues due to the temporal correlation between locations. In order to solve this, we consider

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Liu, Peng, Yan Bai, Lie Wang, and Xianxian Li. "Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing." International Journal of Software Engineering and Knowledge Engineering 27, no. 01 (2017): 71–90. http://dx.doi.org/10.1142/s0218194017500048.

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With the popularity of social networks, privacy issues with regard to publishing social network data have gained intensive focus from academia. We analyzed the current privacy-preserving techniques for publishing social network data and defined a privacy-preserving model with privacy guarantee [Formula: see text]. With our definitions, the existing privacy-preserving methods, [Formula: see text]-anonymity and randomization can be combined together to protect data privacy. We also considered the privacy threat with label information and modify the [Formula: see text]-anonymity technique of tabu

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Mustafa, Raniah Ali, Haitham Salman Chyad, and Jinan Redha Mutar. "Enhancement in privacy preservation in cloud computing using apriori algorithm." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1747–57. https://doi.org/10.11591/ijeecs.v26.i3.pp1747-1757.

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Cloud computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data freque

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Pang, HweeHwa, Xuhua Ding, and Xiaokui Xiao. "Embellishing text search queries to protect user privacy." Proceedings of the VLDB Endowment 3, no. 1-2 (2010): 598–607. http://dx.doi.org/10.14778/1920841.1920918.

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Witten, Ian H., and John G. Cleary. "On the privacy afforded by adaptive text compression." Computers & Security 7, no. 4 (1988): 397–408. http://dx.doi.org/10.1016/0167-4048(88)90580-9.

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Khatamova, Kamola. "MARKETING PRIVACY AND USING TEXT ON ONLINE ADVERTISING." International Journal of Word Art 1, no. 1 (2019): 108–14. http://dx.doi.org/10.26739/2181-9297-2019-1-16.

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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.

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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

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Liu, Gan, Xiongtao Sun, Yiran Li, Hui Li, Shuchang Zhao, and Zhen Guo. "An Automatic Privacy-Aware Framework for Text Data in Online Social Network Based on a Multi-Deep Learning Model." International Journal of Intelligent Systems 2023 (November 8, 2023): 1–23. http://dx.doi.org/10.1155/2023/1727285.

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With the increasing severity of user privacy leaks in online social networks (OSNs), existing privacy protection technologies have difficulty meeting the diverse privacy protection needs of users. Therefore, privacy-aware (PA) for the text data that users post on OSNs has become a current research focus. However, most existing PA algorithms for OSN users only provide the types of privacy disclosures rather than the specific locations of disclosures. Furthermore, although named entity recognition (NER) technology can extract specific locations of privacy text, it has poor recognition performanc

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Fernández Barbudo, Carlos. "Privacidad (digital) = (Digital) Privacy." EUNOMÍA. Revista en Cultura de la Legalidad, no. 17 (September 27, 2019): 276. http://dx.doi.org/10.20318/eunomia.2019.5033.

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Resumen: El desarrollo de las tecnologías de la información, y en particular Internet, ha supuesto la aparición de nuevas preocupaciones sociales que plantean la imposibilidad de preservar la privacidad ―que no la intimidad― de la población en el ámbito digital. Esta contribución aborda, en perspectiva histórica, la formación de un nuevo concepto sociopolítico de privacidad que ha sustituido al de intimidad en el ámbito digital. A tal fin se presentan los principales elementos que diferencian a ambos y cuáles son las transformaciones sociotécnicas fundamentales que han posibilitado este cambio

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Zhang, Kejun, Shaofei Xu, Yutuo Song, et al. "An Efficient Cross-Modal Privacy-Preserving Image–Text Retrieval Scheme." Symmetry 16, no. 8 (2024): 1084. http://dx.doi.org/10.3390/sym16081084.

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Preserving the privacy of the ever-increasing multimedia data on the cloud while providing accurate and fast retrieval services has become a hot topic in information security. However, existing relevant schemes still have significant room for improvement in accuracy and speed. Therefore, this paper proposes a privacy-preserving image–text retrieval scheme called PITR. To enhance model performance with minimal parameter training, we freeze all parameters of a multimodal pre-trained model and incorporate trainable modules along with either a general adapter or a specialized adapter, which are us

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IndraStra, Global Editorial Team. "Chatbots amid Privacy Concerns." IndraStra Global 04, no. 03 (2018): 0057. https://doi.org/10.5281/zenodo.1210022.

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<em>"Chatbots"</em> are highly engaging computer programs through which voice and text messages become a part of a back-and-forth conversation between a user and a user interface. They have gained momentum in past couple of years for driving business outcomes in a cost-effective manner with improved customer experience. These bots can be customized and used on mobile devices, web browsers, and on popular chat platforms such as Facebook Messenger, or Slack.

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Da Silva Perez, Natália. "Privacy and Social Spaces." TSEG - The Low Countries Journal of Social and Economic History 18, no. 3 (2021): 5–16. http://dx.doi.org/10.52024/tseg.11040.

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In this introductory text to the special issue Regulating Access: Privacy and the Private in Early Modern Dutch Contexts, Natália da Silva Perez argues that privacy can be a productive analytical lens to examine the social history of the Dutch Republic. She starts by providing an overview of theoretical definitions of privacy and of the ‘private versus public’ dichotomy, highlighting their implications for the study of society. Next, she discusses the modern view of privacy as a legally protected right, explaining that we must adjust expectations when applying the concept to historical examina

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Bracamonte, Vanessa, Sebastian Pape, and Sascha Loebner. "“All apps do this”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content." Proceedings on Privacy Enhancing Technologies 2022, no. 3 (2022): 57–78. http://dx.doi.org/10.56553/popets-2022-0062.

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Users report that they have regretted accidentally sharing personal information on social media. There have been proposals to help protect the privacy of these users, by providing tools which analyze text or images and detect personal information or privacy disclosure with the objective to alert the user of a privacy risk and transform the content. However, these proposals rely on having access to users’ data and users have reported that they have privacy concerns about the tools themselves. In this study, we investigate whether these privacy concerns are unique to privacy tools or whether the

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Libbi, Claudia Alessandra, Jan Trienes, Dolf Trieschnigg, and Christin Seifert. "Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records." Future Internet 13, no. 5 (2021): 136. http://dx.doi.org/10.3390/fi13050136.

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A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is known to be costly and cumbersome. Synthetic data presents a promising solution to the privacy concern, if synthetic data has comparable utility to real data and if it preserves the privacy of patients. However, the generation of synthetic text alone is not useful for NLP because of the lack of annotations. In this work, we propose the use of neural language

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Ataei, Mehrnaz, Auriol Degbelo, Christian Kray, and Vitor Santos. "Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services." ISPRS International Journal of Geo-Information 7, no. 11 (2018): 442. http://dx.doi.org/10.3390/ijgi7110442.

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An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers

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Mosier, Gregory C. "Text messages: privacy in employee communications in the USA." International Journal of Private Law 2, no. 3 (2009): 260. http://dx.doi.org/10.1504/ijpl.2009.024142.

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D’Acunto, David, Serena Volo, and Raffaele Filieri. "“Most Americans like their privacy.” Exploring privacy concerns through US guests’ reviews." International Journal of Contemporary Hospitality Management 33, no. 8 (2021): 2773–98. http://dx.doi.org/10.1108/ijchm-11-2020-1329.

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Purpose This study aims to explore US hotel guests’ privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly discussed by guests in their reviews and to examine the influence of cultural proximity on privacy concerns. Design/methodology/approach This study combined automated text analytics with content analysis. The database consisted of 68,000 hotel reviews written by US guests lodged in different types of hotels in five European cities. Linguistic Inquiry Word Count, Leximancer and SPSS software were used for data analysis. Au

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Alić, Marta. "Coding the Efficient Privacy Policy: Striking a Balance between Lexical Density and Readability." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (December 23, 2024): 2602–8. https://doi.org/10.37394/23207.2024.21.213.

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Privacy policies play a crucial role in informing individuals about how their personal data is collected, used, and protected. However, the effectiveness of these policies can be hindered by their complexity and lack of readability. This paper aims to explore the relationship between two variables - lexical density and text readability to derive efficient privacy policies text. By striking a balance in document coding the rate of information entropy can be managed, as well as efficiency in transparency tools. The results of privacy policies of 146 healthcare institutions in the Republic of Cro

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Juma’h, Ahmad, Yazan Alnsour, and Hasan Kartal. "The impact of security and privacy perceptions on cryptocurrency app evaluations by users: A text mining study." Investment Management and Financial Innovations 22, no. 1 (2025): 173–87. https://doi.org/10.21511/imfi.22(1).2025.14.

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This study examines how perceived security and privacy influence user ratings of cryptocurrency applications, which are critical for adoption and satisfaction amid the growing popularity of blockchain technologies and rising concerns over information security in online platforms and mobile apps. The study focuses on mobile applications from the Android app market. It used text mining methods to investigate over 64 thousand text-based user reviews and star ratings of over 140 cryptocurrency-related mobile applications available in the Google Play store. Using a partially supervised machine lear

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Ait-Mlouk, Addi, Sadi A. Alawadi, Salman Toor, and Andreas Hellander. "FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning." Applied Sciences 12, no. 6 (2022): 3130. http://dx.doi.org/10.3390/app12063130.

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Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to “understand” a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system c

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Shenigaram, Vidhya, Susheel Kumar Thakur, Choul Praveen Kumar, and Laxman Maddikunta. "SECURE DATA GROUP SHARING AND CONDITIONAL DISSEMINATION WITH MULTI-OWNER IN CLOUD COMPUTING." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 3 (2018): 1312–18. http://dx.doi.org/10.61841/turcomat.v9i3.14475.

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With the rapid development of cloud services, huge volume of data is shared via cloud computing. Although cryptographic techniques have been utilized to provide data confidentiality in cloud computing, current mechanisms cannot enforce privacy concerns over cipher text associated with multiple owners, which makes co-owners unable to appropriately control whether data disseminators can actually disseminate their data. In this paper, we propose a secure data group sharing and conditional dissemination scheme with multi-owner in cloud computing, in which data owner can share private data with a g

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Ramanath, Rohan, Florian Schaub, Shomir Wilson, Fei Liu, Norman Sadeh, and Noah Smith. "Identifying Relevant Text Fragments to Help Crowdsource Privacy Policy Annotations." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (September 5, 2014): 54–55. http://dx.doi.org/10.1609/hcomp.v2i1.13179.

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In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

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Xu, Zifeng, Fucai Zhou, Yuxi Li, Jian Xu, and Qiang Wang. "Privacy-Preserving Subgraph Matching Protocol for Two Parties." International Journal of Foundations of Computer Science 30, no. 04 (2019): 571–88. http://dx.doi.org/10.1142/s0129054119400136.

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Graph data structure has been widely used across many application areas, such as web data, social network, and cheminformatics. The main benefit of storing data as graphs is there exists a rich set of graph algorithms and operations that can be used to solve various computing problems, including pattern matching, data mining, and image processing. Among these graph algorithms, the subgraph isomorphism problem is one of the most fundamental algorithms that can be utilized by many higher level applications. The subgraph isomorphism problem is defined as, given two graphs [Formula: see text] and

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Vadlapati, Praneeth. "TokenizedDB: Text Tokenization using NLP for Enhanced Storage Efficiency and Data Privacy." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem11413.

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SQL databases are widely utilized for data storage across various domains. However, storing text values in SQL databases requires a considerable amount of storage, even if numerous parts of the text are redundant. Traditional compression methods, while helpful, are limited in optimizing storage within databases. Tokenizers used for natural language processing (NLP) can convert text to tokens. This paper proposes a method called TokenizedDB to utilize tokenization to store text as tokens. This approach leads to benefits such as a reduction of storage space. Storage of text as tokens leads to in

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Wei, Weiming, Chunming Tang, and Yucheng Chen. "Efficient Privacy-Preserving K-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation." Entropy 24, no. 8 (2022): 1145. http://dx.doi.org/10.3390/e24081145.

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Privacy-preserving machine learning has become an important study at present due to privacy policies. However, the efficiency gap between the plain-text algorithm and its privacy-preserving version still exists. In this paper, we focus on designing a novel secret-sharing-based K-means clustering algorithm. Particularly, we present an efficient privacy-preserving K-means clustering algorithm based on replicated secret sharing with honest-majority in the semi-honest model. More concretely, the clustering task is outsourced to three semi-honest computing servers. Theoretically, the proposed priva

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Boldt, Martin, and Kaavya Rekanar. "Analysis and Text Classification of Privacy Policies From Rogue and Top-100 Fortune Global Companies." International Journal of Information Security and Privacy 13, no. 2 (2019): 47–66. http://dx.doi.org/10.4018/ijisp.2019040104.

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In the present article, the authors investigate to what extent supervised binary classification can be used to distinguish between legitimate and rogue privacy policies posted on web pages. 15 classification algorithms are evaluated using a data set that consists of 100 privacy policies from legitimate websites (belonging to companies that top the Fortune Global 500 list) as well as 67 policies from rogue websites. A manual analysis of all policy content was performed and clear statistical differences in terms of both length and adherence to seven general privacy principles are found. Privacy

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Ning, Yichen, Na Wang, Aodi Liu, and Xuehui du. "Deep Learning based Privacy Information Identification approach for Unstructured Text." Journal of Physics: Conference Series 1848, no. 1 (2021): 012032. http://dx.doi.org/10.1088/1742-6596/1848/1/012032.

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Resende, Amanda, Davis Railsback, Rafael Dowsley, Anderson C. A. Nascimento, and Diego F. Aranha. "Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation." IEEE Transactions on Information Forensics and Security 17 (2022): 428–42. http://dx.doi.org/10.1109/tifs.2022.3144007.

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M, Priya. "PRIVACY ANALYSIS OF COMMENT USING TEXT MINING IN OSN FRAMEWORK." International Journal of Advanced Research in Computer Science 9, no. 2 (2018): 309–13. http://dx.doi.org/10.26483/ijarcs.v9i2.5765.

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Choi, Daeseon, Younho Lee, Seokhyun Kim, and Pilsung Kang. "Private attribute inference from Facebook’s public text metadata: a case study of Korean users." Industrial Management & Data Systems 117, no. 8 (2017): 1687–706. http://dx.doi.org/10.1108/imds-07-2016-0276.

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Purpose As the number of users on social network services (SNSs) continues to increase at a remarkable rate, privacy and security issues are consistently arising. Although users may not want to disclose their private attributes, these can be inferred from their public behavior on social media. In order to investigate the severity of the leakage of private information in this manner, the purpose of this paper is to present a method to infer undisclosed personal attributes of users based only on the data available on their public profiles on Facebook. Design/methodology/approach Facebook profile

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Zhan, Huixin, and Victor S. Sheng. "Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16143–44. http://dx.doi.org/10.1609/aaai.v37i13.26932.

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Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy concerns when nodes represent people or human-related variables. Moreover, standard NRLs that leverage structural information from a graph proceed by first encoding pairwise relationships into learned representations and then analysing its properties. This approach is fundamentally misaligned with problems where the relationships involve multiple points, and topological structure

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Wang, Qiaozhi, Hao Xue, Fengjun Li, Dongwon Lee, and Bo Luo. "#DontTweetThis: Scoring Private Information in Social Networks." Proceedings on Privacy Enhancing Technologies 2019, no. 4 (2019): 72–92. http://dx.doi.org/10.2478/popets-2019-0059.

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Abstract With the growing popularity of online social networks, a large amount of private or sensitive information has been posted online. In particular, studies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. As such, there exist great needs to be able to identify potentially-sensitive online contents, so that users could be alerted with such findings. In this paper, we propose a context-aware, text-based quantitative model for private information assessment, namely

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Feng, Tao, Xudong Wang, and Xinghua Li. "LBS privacy protection technology based on searchable encryption mechanism." MATEC Web of Conferences 189 (2018): 10013. http://dx.doi.org/10.1051/matecconf/201818910013.

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Location based Service (the Location - -based Service, LBS) is a System is to transform the existing mobile communication network, wireless sensor networks, and Global Positioning System (Global Positioning System, GPS) with the combination of information Service mode, the general improvement in Positioning technology and the high popularity of mobile intelligent terminals, led to the growing market of LBS. This article from the perspective of LBS service privacy security, mainly studies the LBS location privacy protection scheme based on cipher text search, in LBS service location privacy and

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Mehmoona, Jabeen, and Maple Carsten. "Enhancing Security of Text Using Affine Cipher and Image Cryptography." IPSI Transactions on Internet Research 20, no. 2 (2024): 36–43. http://dx.doi.org/10.58245/ipsi.tir.2402.04.

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In the contemporary digital landscape, the escalating reliance on diverse social media platforms for textual communication necessitates the establishment of secure and trustworthy channels to thwart the threats of theft or hacking. Most of these messages contain highly confidential data, underscoring the critical need for robust security measures, primarily through the deployment of encryption techniques. While existing algorithms predominantly employ text-to-text encryption (TOTET) methods, this paper introduces an innovative hybrid approach that amalgamates TOTET with text-to-image encryptio

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Patergianakis, Antonios, and Konstantinos Limniotis. "Privacy Issues in Stylometric Methods." Cryptography 6, no. 2 (2022): 17. http://dx.doi.org/10.3390/cryptography6020017.

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Stylometry is a well-known field, aiming to identify the author of a text, based only on the way she/he writes. Despite its obvious advantages in several areas, such as in historical research or for copyright purposes, it may also yield privacy and personal data protection issues if it is used in specific contexts, without the users being aware of it. It is, therefore, of importance to assess the potential use of stylometry methods, as well as the implications of their use for online privacy protection. This paper aims to present, through relevant experiments, the possibility of the automated

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Wang, Yansheng, Yongxin Tong, and Dingyuan Shi. "Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6283–90. http://dx.doi.org/10.1609/aaai.v34i04.6096.

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Latent Dirichlet Allocation (LDA) is a widely adopted topic model for industrial-grade text mining applications. However, its performance heavily relies on the collection of large amount of text data from users' everyday life for model training. Such data collection risks severe privacy leakage if the data collector is untrustworthy. To protect text data privacy while allowing accurate model training, we investigate federated learning of LDA models. That is, the model is collaboratively trained between an untrustworthy data collector and multiple users, where raw text data of each user are sto

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Al-Rabeeah, Abdullah Abdulabbas Nahi, and Mohammed Mahdi Hashim. "Social Network Privacy Models." Cihan University-Erbil Scientific Journal 3, no. 2 (2019): 92–101. http://dx.doi.org/10.24086/cuesj.v3n2y2019.pp92-101.

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Privacy is a vital research field for social network (SN) sites (SNS), such as Facebook, Twitter, and Google+, where both the number of users and the number of SN applications are sharply growing. Recently, there has been an exponential increase in user-generated text content, mainly in terms of posts, tweets, reviews, and messages on SN. This increase in textual information introduces many problems related to privacy. Privacy is susceptible to personal behavior due to the shared online data structure of SNS. Therefore, this study will conduct a systematic literature review to identify and dis

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Sui, Yi, Xiujuan Wang, Kangfeng Zheng, Yutong Shi, and Siwei Cao. "Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–13. http://dx.doi.org/10.1155/2022/2419987.

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Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker’s personality analysis and reduces the success rate of social engineering attacks. We propose a text transformation method named PerTransGAN using generative adversarial networks (GANs) to protect the personality privacy hidden in text data. Making use of reinforcement learning, we use the output of the discriminator as a reward signal to guide the training of the generator. Moreover, the model extracts text features from the disc

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Ford, Elizabeth, Malcolm Oswald, Lamiece Hassan, Kyle Bozentko, Goran Nenadic, and Jackie Cassell. "Should free-text data in electronic medical records be shared for research? A citizens’ jury study in the UK." Journal of Medical Ethics 46, no. 6 (2020): 367–77. http://dx.doi.org/10.1136/medethics-2019-105472.

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BackgroundUse of routinely collected patient data for research and service planning is an explicit policy of the UK National Health Service and UK government. Much clinical information is recorded in free-text letters, reports and notes. These text data are generally lost to research, due to the increased privacy risk compared with structured data. We conducted a citizens’ jury which asked members of the public whether their medical free-text data should be shared for research for public benefit, to inform an ethical policy.MethodsEighteen citizens took part over 3 days. Jurors heard a range o

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Chen, Caiyun. "Differential Privacy-Enabled TextCNN for MOOCs Fake Review Detection." Journal of Electronic Research and Application 9, no. 1 (2025): 191–201. https://doi.org/10.26689/jera.v9i1.9449.

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The rapid development and widespread adoption of massive open online courses (MOOCs) have indeed had a significant impact on China’s education curriculum. However, the problem of fake reviews and ratings on the platform has seriously affected the authenticity of course evaluations and user trust, requiring effective anomaly detection techniques for screening. The textual characteristics of MOOCs reviews, such as varying lengths and diverse emotional tendencies, have brought complexity to text analysis. Traditional rule-based analysis methods are often inadequate in dealing with such unstructur

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