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Relevant bibliographies by topics / Text privacy

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Academic literature on the topic 'Text privacy'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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Journal articles on the topic "Text privacy"

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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, analyses could become practically useless. The trade-off between value and privacy should, however, become more manageable as the size of the data set increase. Along these lines, in this paper, the fundamental ideas of sensitivity and privacy budget in differential privacy, the noise mechanisms utilized as a part of differential privacy, the composition properties, the ways through which it can be achieved and the developments in this field to date have been presented.

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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 that maximize data utility retention and guarantee privacy. To this end, I aim to study and develop methods to incorporate steganographic modifications within the vector geometry to obfuscate underlying spurious associations and retain the distributional semantic properties learnt during training.

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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 speed when used with NLG models due to the neglect of time-consuming operations in auto-regressive generations. To address these issues, we propose a fast private text generation framework for Transformer-based language models, namely MERGE. MERGE reuses the output hidden state as the word embedding to bypass the embedding computation and reorganize the linear operations in the Transformer module to accelerate the forward procedure. Extensive experiments show that MERGE achieves a 26.5x speedup to the vanilla encrypted model under the sequence length 512, and reduces 80% communication cost, with an up to 10x speedup to state-of-the-art approximated models.

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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 guarantees, and it identifies neural network architectures that offer superior trade-offs. To this end, we use a white-box membership inference attack to empirically assess the information leakage of three widely used neural network architectures. We show that large differential privacy parameters already suffice to completely mitigate membership inference attacks, thus resulting only in a moderate decrease in model utility. More specifically, for large datasets with long texts, we observed Transformer-based models to achieve an overall favorable privacy–utility trade-off, while for smaller datasets with shorter texts, convolutional neural networks are preferable.

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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 of the text are encrypted using distinct tokens to ensure a variation in encryption patterns throughout the resultant text. The process enhances data security and privacy using an unconventional approach that makes it secure from attackers attempting to reconstruct the text. The system can be used in addition to the existing encryption approaches. The results show a successful encryption of text. Weak passwords are successfully encrypted to create strong passwords with multiple types of characters, including alphabets, numbers, and special characters. The code is available at github.com/Pro-GenAI/TokEncryption. Keywords: data privacy, data security, data encryption, natural language processing (NLP), tokenization, cryptography, information security

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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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Dissertations / Theses on the topic "Text privacy"

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Aryasomayajula, Naga Srinivasa Baradwaj. "Machine Learning Models for Categorizing Privacy Policy Text." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535633397362514.

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Hammoud, Khodor. "Trust in online data : privacy in text, and semantic-based author verification in micro-messages." Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5203.

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De nombreux problèmes émanent de la diffusion et l'utilisation des données sur les réseaux sociaux. Il est nécessaire de promouvoir la confiance sur les plateformes sociales, quant au partage et l’utilisation des données. Les données en ligne sont principalement sous forme textuelle, ce qui pose des problèmes aux solutions d'automatisation en raison de la richesse du langage naturel. De plus, l'utilisation des micro-messages comme principal moyen de communication sur les médias sociaux rend le problème beaucoup plus difficile en raison de la rareté des fonctionnalités à analyser par corps de texte. Nos expériences montrent que les solutions d'anonymat des données ne peuvent pas préserver l'anonymat des utilisateurs sans sacrifier la qualité des données. De plus, dans le domaine de la vérification d'auteur, étant donné un ensemble de documents dont l'auteur est connu, nous avons constaté très peu de travaux de recherche travaillant sur les micro-messages. Nous avons également remarqué que l'état de l'art ne prend pas en considération la sémantique des textes, les rendant vulnérables aux attaques par usurpation d'identité. Motivés par ces résultats, nous consacrons cette thèse pour aborder les tâches de (1) identifier les problèmes actuels avec l'anonymat des données utilisateur dans le texte, et fournir une première approche sémantique originale pour résoudre ce problème, (2) étudier la vérification de l'auteur en micro -messages, et développer une nouvelle approche basée sur la sémantique pour résoudre ces défis, et (3) étudier l'effet de l'inclusion de la sémantique dans la gestion des attaques de manipulation, (4) étudier l'effet temporel des données, où les auteurs pourraient avoir changer d'avis au fil du temps. La première partie de la thèse se concentre sur l'anonymat des utilisateurs dans les données textuelles sur les réseaux sociaux, dans le but d'anonymiser les informations personnelles des données des utilisateurs en ligne pour une analyse sécurisée des données sans compromettre la confidentialité des utilisateurs. Nous présentons une première approche basée sur la sémantique, qui peut être personnalisée pour équilibrer la préservation de la qualité des données et la maximisation de l'anonymat de l'utilisateur en fonction de l'application à portée de main. Dans la deuxième partie, nous étudions la vérification d'auteur dans les micro-messages sur les réseaux sociaux. Nous confirmons le manque de recherche en vérification d'auteur sur les micro-messages, et nous montrons que l'état de l'art ne fonctionne pas bien lorsqu'il est appliqué sur des micro-messages. Ensuite, nous présentons une nouvelle approche basée sur la sémantique qui utilise des inclusions de mots et une analyse des sentiments pour collecter l'historique des opinions de l'auteur afin de déterminer l'exactitude de la revendication de paternité et montrer ses performances concurrentielles sur les micro-messages. Nous utilisons ces résultats dans la troisième partie de la thèse pour améliorer encore notre approche. Nous construisons un ensemble de données composé des tweets des 88 influenceurs Twitter les plus suivis. Nous l'utilisons pour montrer que l'état de l'art n'est pas capable de gérer les attaques d'usurpation d'identité, modifiant le message derrière le tweet, tandis que le modèle d'écriture est préservé. D'autre part, puisque notre approche est consciente de la sémantique du texte, elle est capable de détecter les manipulations de texte avec une précision supérieure à 90%. Et dans la quatrième partie de la thèse, nous analysons l'effet temporel des données sur notre approche de vérification d'auteur.Nous étudions l'évolution des opinions des auteurs au fil du temps et comment s'en accommoder dans notre approche. Nous étudions les tendances des sentiments d'un auteur pour un sujet spécifique sur une période de temps et prédisons les fausses allégations de paternité en fonction de la période dans laquelle se situe la revendication<br>Many Problems surround the spread and use of data on social media. There is a need to promote trust on social platforms, regarding the sharing and consumption of data. Data online is mostly in textual form which poses challenges for automation solutions because of the richness of natural language. In addition, the use of micro-messages as the main means of communication on social media makes the problem much more challenging because of the scarceness of features to analyze per body of text. Our experiments show that data anonymity solutions cannot preserve user anonymity without sacrificing data quality. In addition, in the field of author verification, which is the problem of determining if a body of text was written by a specific person or not, given a set of documents known to be authored by them, we found a lack of research working with micro-messages. We also noticed that the state-of-the-art does not take text semantics into consideration, making them vulnerable to impersonation attacks. Motivated by these findings, we devote this thesis to tackle the tasks of (1) identifying the current problems with user data anonymity in text, and provide an initial novel semantic-based approach to tackle this problem, (2) study author verification in micro-messages and identify the challenges in this field, and develop a novel semantics-based approach to solve these challenges, and (3) study the effect of including semantics in handling manipulation attacks, and the temporal effect of data, where the authors might have changing opinions over time. The first part of the thesis focuses on user anonymity in textual data, with the aim to anonymize personal information from online user data for safe data analysis without compromising users’ privacy. We present an initial novel semantic-based approach, which can be customized to balance between preserving data quality and maximizing user anonymity depending on the application at hand. In the second part, we study author verification in micro-messages on social media. We confirm the lack of research in author verification on micro-messages, and we show that the state-of-the-art, which primarily handles long and medium-sized texts, does not perform well when applied on micro-messages. Then we present a semantics-based novel approach which uses word embeddings and sentiment analysis to collect the author’s opinion history to determine the correctness of the claim of authorship, and show its competitive performance on micro-messages. We use these results in the third part of the thesis to further improve upon our approach. We construct a dataset consisting of the tweets of the 88 most followed twitter influencers. We use it to show that the state-of-the-art is not able to handle impersonation attacks, where the content of a tweet is altered, changing the message behind the tweet, while the writing pattern is preserved. On the other hand, since our approach is aware of the text’s semantics, it is able to detect text manipulations with an accuracy above 90%. And in the fourth part of the thesis, we analyze the temporal effect of data on our approach for author verification. We study the change of authors’ opinions over time, and how to accommodate for that in our approach. We study trends of sentiments of an author per a specific topic over a period of time, and predict false authorship claims depending on what timeframe does the claim of authorship fall in

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Connelly, Eric M. "[Redacted Text] and Surveillance: An Ideographic Analysis of the Struggle between National Security and Privacy." Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/communication_theses/66.

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In the aftermath of the events of 9/11, the U.S. executive branch has repeatedly maintained that its need for action to secure the nation requires a revised interpretation of individual liberties. This study will explore the tensions between the positive ideographs and in response to the negative ideograph in a contemporary United States court ruling. Using Burke’s pentad, and cluster analysis, as well as Brummett’s notion of strategic silence, the study examines how the FISCR substantially changed the interrelationship between the two ideographs. The study concludes that the FISCR situated strengthening national security as the purpose of the case it ruled on, which privileged national security over privacy. Throughout the expansion of security,> the court used silence to justify its decision. This analysis both adds to our understanding of the synchronic relationship between ideographs, and examines how the courts utilize such interplays to reconstitute community.

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Rekanar, Kaavya. "Text Classification of Legitimate and Rogue online Privacy Policies : Manual Analysis and a Machine Learning Experimental Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13363.

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Mysore, Gopinath Abhijith Athreya. "Automatic Detection of Section Title and Prose Text in HTML Documents Using Unsupervised and Supervised Learning." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535371714338677.

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Liu, Meng-Chang. "Achieving privacy-preserving distributed statistical computation." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/achieving-privacypreserving-distributed-statistical-computation(6831db5c-d605-4a38-9711-7592d2b94e01).html.

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The growth of the Internet has opened up tremendous opportunities for cooperative computations where the results depend on the private data inputs of distributed participating parties. In most cases, such computations are performed by multiple mutually untrusting parties. This has led the research community into studying methods for performing computation across the Internet securely and efficiently. This thesis investigates security methods in the search for an optimum solution to privacy- preserving distributed statistical computation problems. For this purpose, the nonparametric sign test algorithm is chosen as a case for study to demonstrate our research methodology. Two privacy-preserving protocol suites using data perturbation techniques and cryptographic primitives are designed. The first protocol suite, i.e. the P22NSTP, is based on five novel data perturbation building blocks, i.e. the random probability density function generation protocol (RpdfGP), the data obscuring protocol (DOP), the secure two-party comparison protocol (STCP), the data extraction protocol (DEP) and the permutation reverse protocol (PRP). This protocol suite enables two parties to efficiently and securely perform the sign test computation without the use of a third party. The second protocol suite, i.e. the P22NSTC, uses an additively homomorphic encryption scheme and two novel building blocks, i.e. the data separation protocol (DSP) and data randomization protocol (DRP). With some assistance from an on-line STTP, this protocol suite provides an alternative solution for two parties to achieve a secure privacy-preserving nonparametric sign test computation. These two protocol suites have been implemented using MATLAB software. Their implementations are evaluated and compared against the sign test computation algorithm on an ideal trusted third party model (TTP-NST) in terms of security, computation and communication overheads and protocol execution times. By managing the level of noise data item addition, the P22NSTP can achieve specific levels of privacy protection to fit particular computation scenarios. Alternatively, the P22NSTC provides a more secure solution than the P22NSTP by employing an on-line STTP. The level of privacy protection relies on the use of an additively homomorphic encryption scheme, DSP and DRP. A four-phase privacy-preserving transformation methodology has also been demonstrated; it includes data privacy definition, statistical algorithm decomposition, solution design and solution implementation.

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Steiner, Wolfgang Ernst. "Justifying limitations on privacy: the influence of the proportionality test in South African and German law." Master's thesis, University of Cape Town, 2013. http://hdl.handle.net/11427/4738.

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Baldwin, Lind Paula. "Looking for privacy in Shakespeare : woman's place and space in a selection of plays and early modern texts." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5848/.

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Grounded in a multi-faceted theoretical framework that examines the dynamic interaction between the public and the private spheres of Elizabethan everyday life, this thesis aims to trace how the concept of privacy and its associated terms were developed, constructed, evoked, and configured both in Shakespearean drama and in other illustrative early modern texts. The author suggests that Shakespeare's configuration of space results from a combination of the conditions of representation - empty stages - metaphorical language, technical dramatic devices, and textual markers that create a sense of space in the texts and onstage. The research also explores the place and space of early modern women and of Shakespeare's female characters in terms of their relation to the private space; that is to say, their construction of 'self-in-relation-to-space', as well as their movements and activities within and outside the private's real or imagined boundaries, thus their ability to fashion the public sphere from within the private. Rather than analysing the role of women in the plays exclusively from the point of view of opposition between spheres - public man versus private woman - the study wants to question and pose, at the same time, the relevance of approaching Shakespearean texts from a spatial perspective, a choice that may have an impact on the very interpretation of them.

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Chow, Mark A. (Mark Andrew) 1972. "A rating system to test private investment decisions in public infrastructure projects." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50511.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.<br>Includes bibliographical references (p. 167-168).<br>This thesis will develop a basic method to evaluate the overall quality of proposed infrastructure projects for private sector financial investment. INFRATEST is meant to aid both potential private infrastructure developers and public entities, which desire to privatize certain infrastructure projects, in selection of the most appropriate infrastructure projects to benefit from the advantages of free enterprise. INFRATEST is premised on 15 equally-weighted factors which represent the major components that affect overall infrastructure project economic, financial, and technical viability. Associated with each of the 15 factors are indicators which measure the important aspects of their respective factors. There are 31 indicators in all and they are evaluated on a numerical scale of one to ten. Factor scores are determined from indicator value averages. INFRATEST can serve the private developer and the public entity by providing an information base for deciding which privately funded infrastructure development proposals deserve consideration in the capital markets and for deciding which proposed infrastructure projects are to be developed with public or private funds. Application of INFRATEST to two real-world project proposals, the SAVE project and the Northumberland Bridge project, demonstrated the method's ease and universality of application as well as the method's simple and clear conclusions.<br>by Mark A. Chow.<br>S.M.

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Brodin, Gustav. "Privatekonomi i läroböcker : En läroboksstudie i samhällsvetenskap om privatekonomins kvantitet och innehåll." Thesis, Karlstads universitet, Institutionen för samhälls- och kulturvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-28147.

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Barn och ungas skulder som registrerats hos Kronofogden har under de senaste åren aldrig varit flera. Skolan har ett ansvar att försöka hindra denna oroväckande utveckling. Denna uppsats handlar om privatekonomins förekomst i tre olika läroböcker för samhällskunskap 1A1 på gymnasiet och har som mål att undersöka vilken hjälp dessa böcker ger lärare och elever på gymnasiet. I den senaste läroplanen SKOLFS 2011 har privatekonomi lagts till och är numera ett gymnasiegemensamt ämne som alla elever läser. Genom ett kodschema har jag kunnat identifiera förekomsten av privatekonomin och bestämma kvantiteten av densamma genom räknandet av ord. Undersökningen är tvådelad där den andra delen analyserar innehållet och försöker göra slutsatser genom olika typer av textanalyser. Slutsatsen för undersökningen är att de tre böckerna har olika styrkor och svagheter men framförallt är det språkbruket som skiljer sig åt. Ett lärobokstext bör inte kräva allt för stora förkunskaper eller läsförståelse i en A-kurs. Två av böckerna hanterar detta väl medan den tredje lyckas mindre bra.<br>The debts of children and teenagers that are registered at the Swedish Enforcement Authority, have in the past few years hit an all-time high. School has a responsibility to try and stop this disturbing development. This essay is about private economy and its existence in three different textbooks for civics 1A1 and the Swedish high school. The goal is to investigate which support these books can give teachers and students. Private economy is a recent addition to the Swedish teaching program 2011 and is now read by all high school students. I have been able to identify the existence of private economy in textbooks by creating a code schedule. This way I have been able to decide the quantitative by simply counting words. The review is categorical in two parts where the second part illuminating the content and tries to make conclusions by different types of text analyses. The conclusion of this essay is that the books have its differences, both strengths and weaknesses. It is mostly the use of language that differs. A textbook in civics does not demand great text understanding. Two of the books handle this well, while the third one does not.

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Books on the topic "Text privacy"

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Aryeh Greenfield-A.G. Publications (Israel), ed. Protection of Privacy: Full text English translation of the Protection of Privacy Law 5741-1981 and of relevant subsidiary legislation correct as of November 1, 2011. 6th ed. Aryeh Greenfield-A.G. Publications, 2011.

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United States. Federal Aviation Administration., ed. Private pilot test guide. Aviation Supplies & Academics, 1992.

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McCarthy, Steven J. Unbroken record: Electronic communication, forever undigitized. Steven McCarthy, 2014.

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United States. Federal Aviation Administration. Office of Flight Standards., ed. Private pilot: Practical test standards. U.S. Dept. of Transportation, Federal Aviation Administration, Office of Flight Standards, 1987.

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United States. Federal Aviation Administration. Office of Flight Standards Service., ed. Private pilot: Practical test standards. Flight Standards Service, 1995.

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United States. Federal Aviation Administration. Office of Flight Standards Service., ed. Private pilot: Practical test standards. Flight Standards Service, 1995.

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Benson, Peter. Theory of private law: Selected topics and text. Faculty of Law, University of Toronto, 2013.

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United States. Health Care Financing Administration. Medicare and private health insurance: A training text. U.S. Department of Health and Human Services, Health Care Financing Administration, 1985.

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Benson, Peter. Theory of private law: Selected topics and text. Faculty of Law, University of Toronto, 2014.

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Benson, Peter. Theory of private law: Selected topics and text. Faculty of Law, University of Toronto, 2010.

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Book chapters on the topic "Text privacy"

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Salomon, David. "Data Hiding in Text." In Data Privacy and Security. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21707-9_11.

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Hart, Michael, Pratyusa Manadhata, and Rob Johnson. "Text Classification for Data Loss Prevention." In Privacy Enhancing Technologies. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22263-4_2.

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Garg, Vaibhav, L. Jean Camp, Katherine Connelly, and Lesa Lorenzen-Huber. "Risk Communication Design: Video vs. Text." In Privacy Enhancing Technologies. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31680-7_15.

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Danezis, George, Claudia Diaz, Carmela Troncoso, and Ben Laurie. "$\text{Drac}$ : An Architecture for Anonymous Low-Volume Communications." In Privacy Enhancing Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14527-8_12.

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Müller, Nicolas M., Daniel Kowatsch, Pascal Debus, Donika Mirdita, and Konstantin Böttinger. "On GDPR Compliance of Companies’ Privacy Policies." In Text, Speech, and Dialogue. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27947-9_13.

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Haralabopoulos, Giannis, Mercedes Torres Torres, Ioannis Anagnostopoulos, and Derek McAuley. "Privacy-Preserving Text Labelling Through Crowdsourcing." In Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79157-5_35.

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Manzanares-Salor, Benet, David Sánchez, and Pierre Lison. "Automatic Evaluation of Disclosure Risks of Text Anonymization Methods." In Privacy in Statistical Databases. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13945-1_12.

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Dalianis, Hercules. "Ethics and Privacy of Patient Records for Clinical Text Mining Research." In Clinical Text Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78503-5_9.

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Fernandes, Natasha, Mark Dras, and Annabelle McIver. "Generalised Differential Privacy for Text Document Processing." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17138-4_6.

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Gusain, Vaibhav, and Douglas Leith. "Towards Quantifying the Privacy of Redacted Text." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28238-6_32.

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Conference papers on the topic "Text privacy"

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Zhan, Huixin, Liyuan Gao, and Victor S. Sheng. "Privacy-Preserving Unsupervised Spherical Text Embeddings." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650028.

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Yang, Yuchen, Bo Hui, Haolin Yuan, Neil Gong, and Yinzhi Cao. "SneakyPrompt: Jailbreaking Text-to-image Generative Models." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00123.

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Papadopoulou, Anthi, Yunhao Yu, Pierre Lison, and Lilja Øvrelid. "Neural Text Sanitization with Explicit Measures of Privacy Risk." In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.aacl-main.18.

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Liu, Shuofeng, Mengyao Ma, Minhui Xue, and Guangdong Bai. "Modifier Unlocked: Jailbreaking Text-to-Image Models Through Prompts." In 2025 IEEE Symposium on Security and Privacy (SP). IEEE, 2025. https://doi.org/10.1109/sp61157.2025.00242.

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Zhang, Xinyu, Hanbin Hong, Yuan Hong, et al. "Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00053.

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Shan, Shawn, Wenxin Ding, Josephine Passananti, Stanley Wu, Haitao Zheng, and Ben Y. Zhao. "Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00207.

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Shahriar, Sakib, and Rozita Dara. "PriSM: Privacy-Preserving Social Media Text Processing and Analytics Framework." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00014.

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Meisenbacher, Stephen, Alexandra Klymenko, Alexander Karpp, and Florian Matthes. "Investigating User Perspectives on Differentially Private Text Privatization." In Proceedings of the Sixth Workshop on Privacy in Natural Language Processing. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.privatenlp-main.8.

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Arnold, Stefan. "Inspecting the Representation Manifold of Differentially-Private Text." In Proceedings of the Sixth Workshop on Privacy in Natural Language Processing. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.privatenlp-main.5.

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Wang, WenHao, Xiaoyu Liang, Rui Ye, Jingyi Chai, Siheng Chen, and Yanfeng Wang. "KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.438.

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Reports on the topic "Text privacy"

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Maerz, Seraphine. Using AI for Text Analysis: Advanced Applications. Instats Inc., 2024. https://doi.org/10.61700/nqumt3vu2zq3k1816.

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This one-day workshop provides an advanced exploration of AI-driven text analysis, focusing on the application and fine-tuning of open-source large language models to enhance research across various academic disciplines. Participants will gain practical skills in setting up local environments and employing sophisticated AI methodologies, empowering them to conduct independent and impactful text analysis while ensuring data security and privacy.

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Cantor, Amy G., Rebecca M. Jungbauer, Andrea C. Skelly, et al. Respectful Maternity Care: Dissemination and Implementation of Perinatal Safety Culture To Improve Equitable Maternal Healthcare Delivery and Outcomes. Agency for Healthcare Research and Quality (AHRQ), 2024. http://dx.doi.org/10.23970/ahrqepccer269.

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Objective. To summarize current research defining and measuring respectful maternity care (RMC) and evaluate the effectiveness of RMC and implementation strategies to improve health outcomes, particularly for populations at risk for health disparities. Data sources. Ovid MEDLINE®, Embase®, and Cochrane CENTRAL from inception to November 2022 and SocINDEX to July 2023; manual review of reference lists and responses to a Federal Register Notice. Review methods. Dual review of eligible abstracts and full-text articles using predefined criteria. Data abstraction and quality assessment dual reviewed using established methods. Systematic evaluation of psychometric studies of RMC tools using adapted criteria. Meta-analysis not conducted due to heterogeneity of studies and limited data. Results. Searches identified 4,043 unique records. Thirty-seven studies were included across all questions, including the Contextual Question (CQ). Twenty-four validation studies (3 observational studies, 21 cross-sectional studies) evaluated 12 tools for measuring RMC. One randomized controlled trial (RCT) evaluated RMC effectiveness. There were no effectiveness trials from settings relevant to clinical practice in the United States and no studies evaluating effectiveness of RMC implementation. For the CQ, 12 studies defined 12 RMC frameworks. Two types of frameworks defined RMC: (1) Disrespect and Abuse (D&A) and (2) Rights-Based. Components of D&A frameworks served as indicators for recognizing mistreatment during childbirth, while Rights-Based frameworks incorporated aspects of reproductive justice, human rights, and anti-racism. Overlapping themes from RMC frameworks included: freedom from abuse, consent, privacy, dignity, communication, safety, and justice. Tools that measured RMC performed well based on psychometric measures, but no single tool stood out as the best measure of RMC. The intrapartum version of the Mother’s Autonomy in Decision-Making (MADM), Mothers On Respect index (MORi), and the Childbirth Options, Information, and Person-Centered Explanation (CHOICES) index for measuring RMC demonstrated good overall validity based on analysis of psychometric properties and were applicable to U.S. populations. The Revised Childbirth Experience Questionnaire (CEQ-2) demonstrated good overall validity for measuring childbirth experiences and included RMC components. One fair-quality RCT from Iran demonstrated lower rates of postpartum depression at 6-8 weeks for those who received RMC compared with controls (20% [11/55] vs. 50% [27/54], p=0.001), measured by the Edinburgh Postpartum Depression Scale. No studies evaluated any other health outcomes or measured the effectiveness of RMC implementation strategies. Conclusions. RMC frameworks with overlapping components, themes, and definitions were well described in the literature, but consensus around one operational definition is needed. Validated tools to measure RMC performed well based on psychometric measures but have been subject to limited evaluation. A reliable metric informed by a standard definition could lead to further evaluation and implementation in U.S. settings. Evidence is currently lacking on the effectiveness of strategies to implement RMC to improve any maternal or infant health outcome.

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Andrabi, Tahir, Natalie Bau, Jishnu Das, and Asim I. Khwaja. Heterogeneity in School Value-Added and the Private Premium. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/116.

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Using rich panel data from Pakistan, we compute test score based measures of quality (School Value-Addeds or SVAs) for more than 800 schools across 112 villages and verify that they are valid and unbiased. With the SVA measures, we then document three striking features of the schooling environment. First, there is substantial within-village variation in quality. The annualized difference in learning between the best and worst performing school in the same village is 0.4 sd; compounded over 5 years of primary schooling, this difference is similar in size to the test score gap between low- and high-income countries. Second, students learn more in private schools (0.15 sd per year on average), but substantial within-sector variation in quality means that the effects of reallocating students from public to private schools can range from -0.35sd to +0.65sd. Thus, there is a range of possible causal estimates of the private premium, a feature of the environment we illustrate using three different identification approaches. Finally, parents appear to recognize and reward SVA in the private sector, but the link between parental demand and SVA is weaker in the public sector. These results have implications for both the measurement of the private premium and how we design and evaluate policies that reallocate children across schools, such as school closures and vouchers.

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Agüero, Jorge M., and Verónica Frisancho. Systematic Bias in Sensitive Health Behaviors and Its Impact on Treatment Effects: An Application to Violence against Women. Inter-American Development Bank, 2017. http://dx.doi.org/10.18235/0007031.

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Violence against women takes place mainly in the private sphere and isperpetrated by people close to the victim. These features can introduce large biases into its reporting in specialized surveys as well as to the authorities. We test for the existence of measurement error in the reporting of such violence using experimental methods in Peru, a country with several specialized surveys but one lacking reliable administrative data. We ask women to report past experiences of violent acts by randomly assigning them one of two questionnaires, one that replicates current surveys and another that relies on list experiments to provide a more private setting. We find no significant reporting bias on average. However, we uncover strong evidence of non-random measurement error by education level. For highly educated women, an increase in privacy leads to higher reporting of violence, while no change is observed for the less educated. The increase is large enough to reverse the education gradient in violence. We discuss how nonclassical error in the outcome variable affects the estimation of the role of risk factors on violence. In particular, randomized controlled trials underperform instrumental variables estimates and, under certain conditions, the former could lead to even larger biases compared to cross-sectional studies.

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Andrabi, Tahir, Natalie Bau, Jishnu Das, Naureen Karachiwalla, and Asim I. Khwaja. Crowding in Private Quality: The Equilibrium Effects of Public Spending in Education. Research on Improving Systems of Education (RISE), 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/124.

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We estimate the equilibrium effects of a public school grant program administered through school councils in Pakistani villages with multiple public and private schools and clearly defined catchment boundaries. The program was randomized at the village-level, allowing us to estimate its causal impact on the market. Four years after the start of the program, test scores were 0.2 sd higher in public schools. We find evidence of an education multiplier: test scores in private schools were also 0.2 sd higher in treated markets. Consistent with standard models of product differentiation, the education multiplier is greater for those private schools that faced a greater threat to their market power. Accounting for private sector responses increases the program’s cost effectiveness by 85 percent and affects how a policymaker would target spending. Given that markets with several public and private schools are now pervasive in low- and middle-income countries, prudent policy requires us to account for private sector responses to public policy, both in their design and in their evaluation.

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Bonaldi, Pietro, and Mauricio Villamizar-Villegas. An Auction-Based Test of Private Information in an Interdealer FX Market. Banco de la República, 2018. http://dx.doi.org/10.32468/be.1049.

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Bronnenberg, Bart, Jean-Pierre Dubé, and Robert Sanders. Consumer Misinformation and the Brand Premium: A Private Label Blind Taste Test. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w25214.

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Ahwireng-Obeng, Asabea Shirley, and Frederick Ahwireng-Obeng. Private Philanthropic Cross-Border Flows and Sustainable Development in Africa. Centre on African Philanthropy and Social Investment, 2011. http://dx.doi.org/10.47019/2021.ra1.

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The paper examines the simultaneous impact of private philanthropic cross-border funding from international foundations on the economic, social, and environmental dimensions of sustainable development in Africa. The vector error correction model (VECM) was used, and contrary to expectations drawn from past studies, funding from this source improves economic growth, advances human development, and enhances environmental quality. Causality test results also disconfirmed the assumption that interactions among the three dimensions were positive and complementary in the long term. The environment variable was found to be noncomplementary. Based on these unique results, theoretical propositions are made with an underlying mechanism of action. Practical and policy implications, limitations, and directions for future research are discussed.

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Gan, Li, Feng Huang, and Adalbert Mayer. A Simple Test of Private Information in the Insurance Markets with Heterogeneous Insurance Demand. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w16738.

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Borger, Michael, Gregory Elacqua, Isabel Jacas, Christopher Neilson, and Anne Sofie Westh Olsen. Report Cards: Parental Preferences, Information and School Choice in Haiti. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004933.

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This paper studies school choice and information in the context of education markets in rural Haiti. Using a market level randomized control trial, we evaluate the aggregate effect of providing test score information on subsequent test scores, prices, and enrollment. After the intervention, we find that private schools have higher test scores, with an average increase of 0.3 standard deviations in treated markets. However, we are unable to detect significant changes to prices and market shares. These findings suggest that providing information in poor education markets can improve market efficiency and benefit children's welfare.

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