Using natural language processing to detect privacy violations in online contracts

Silva, Paulo; Goncalves, C. S.; Godinho, Carolina; Antunes, Nuno; Curado, Marília

doi:10.1145/3341105.3375774

Cited by 8 publications

(6 citation statements)

References 7 publications

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“…Through the application of named entity recognition (NER) algorithms, machine learning models can precisely categorize and label con dential details within both generic and context-speci c datasets [70] [71]. This strategy functions as a Privacy Enhancing Technology (PET), automatically obscuring sensitive data and upholding the right to privacy [72]. The utilization of NLP tools, such as NLTK, Stanford CoreNLP, and spaCy, in conjunction with machine learning models, has yielded favorable outcomes in the accurate classi cation of PII within contracts and documents [73][74].…”

Section: Arti Cial Intelligence In Smart Contracts For Safeguardingmentioning

confidence: 99%

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

Yaseen,

Hassan

2024

Preprint

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The extensive implementation of smart contracts in blockchain ecosystems has brought significant benefits, particularly in enhancing transparency and transactional effectiveness. However, this remarkable advancement has also raised substantial concerns regarding the protection of sensitive data during smart contract execution. This research analysis delves into an in-depth examination of the constantly evolving domain of data privacy in the realm of smart contracts, going beyond the inherent transparency that defines blockchain technology. In this pursuit, we embark on an exploration of existing literature through a bibliometric analysis, shedding light on the trends and developments in this crucial field of study. Our objective is to discern the key challenges and issues confronting data privacy. Through a systematic bibliometric analysis, we aim to identify and evaluate the efficacy of current privacy-preserving mechanisms while pinpointing potential avenues for future research endeavors to address privacy concerns in smart contracts comprehensively. This literature review, informed by bibliometric insights, not only advances our collective understanding of data privacy within smart contracts but also offers invaluable guidance to developers, researchers, and policymakers seeking to shape and implement privacy-enhancing measures in forthcoming blockchain-based systems.

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Section: Arti Cial Intelligence In Smart Contracts For Safeguardingmentioning

confidence: 99%

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

Yaseen,

Hassan

2024

Preprint

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“…The paper titled "Using Natural Language Processing to Detect Privacy Violations in Online Contracts" by P. Silva et.al. [35] and another paper titled "Extractive Automatic Text Summarization using SpaCy in Python & NLP" [36] made comparison between spaCy, coreNLP and NLTK and came to conclusion that coreNLP has the best performance in terms of precision, recall and F1 score followed by spaCy and lastly NLTK. SpaCy is supported in number of programming languages like "R", "ruby", "cpp", "java script", ".net", "python", etc.…”

Section: B Nltk Vs Spacymentioning

confidence: 99%

NLI-GSC: A Natural Language Interface for Generating SourceCode

Ansari¹,

Vora²

2022

IJACSA

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There are many different programming languages and each programming language has its own structure or way of writing the code, it becomes difficult to learn and frequently switch between different programming languages. Due to this reason, a person working with multiple programming languages needs to look at documentations frequently which costs time and effort. In the past few years, there have been significant increase in the amount of papers published on this topic, each providing a unique solution to this problem. Many of these papers are based on applying NLP concepts in unique configuration to get the desired results. Some have used AI along with NLP to train the system to generate source-code in specific language, and some have trained the AI directly without pre-processing the dataset with NLP. All of these papers face two problems: a lack of proper dataset for this particular application and each paper can convent natural language into only one specified programming language source-code. This proposed system shows that a language independent solution is a feasible alternate for writing source-code without having full knowledge about a programming language. The proposed system uses Natural Language Processing to convert Natural Language into programming language-independent pseudo code using custom Named Entity Recognition and save it in XML (eXtensible Markup Language) format which is an intermediate step. Then, using traditional programming, this system converts the generated pseudo code into programming language-dependent source-code. In this paper, another novel method has been proposed to create dataset from scratch using predefined structure that is filled with predefined keywords creating unique combination of training dataset.

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“…NLP can be used to extract and process clinical data or information for both structured and unstructured forms. NLP also could be used for patient's classification and supporting critical clinical tasks like clinical decisionmaking and producing quality reports [10] . A clinical decision-making system can be developed using patient's behavior toward a product, medicine, or treatment using NLP techniques.…”

Section: Nlp Applicationsmentioning

confidence: 99%

Natural language processing: A literature survey

Jain¹

2020

Int. J. Commun. Inf. Technol.

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It is quite hard to imagine a smart system like a voice assistant or a chat-bot or a recommender system without Natural Language Processing (NLP). It all starts with an initial unit that first interprets the data (audio or text) provided and then start making sense of the data and after proper processing of the data the actual steps are followed by the machine to throw some replies or get the work done. NLP does not fall under a discipline; rather it is a part of several different disciplines i.e. computer science, information engineering, Artificial Intelligence (AI), and linguistics. The concern of NLP is the interaction between a computer and human languages. NLP areas include Speech Recognition, Machine Translation, Automatic Text Summarization, Part-of-Speech Tagging, etc. Generally, NLP is used in many real time applications like smart homes, smart offices like Alexa, Cortana, Siri, and Google Assistant. The history of NLP generally started in the 1950s and has come a long way from then and improved a lot. This paper discusses the history of NLP, its evolution, its tools and techniques, and its applications in different fields. The paper also discusses the role of Machine Learning and Artificial Neural Networks (ANNs) to improve NLP.

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Using natural language processing to detect privacy violations in online contracts

Cited by 8 publications

References 7 publications

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

A Comprehensive Survey Integrating Scientometric Analysis and ML approaches for Data Protection

NLI-GSC: A Natural Language Interface for Generating SourceCode

Natural language processing: A literature survey

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