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27 results for “data privacy”
Privacy Policies Paragraph containing Personal Data
<p>The data consists in crawled privacy policies from European privacy policies. They were split into paragraphs and annotated as containing or not personal data.</p> <p>The question that was asked to annotators was "Does this paragraph contain the explicit mention of specific personal data (e.g. name, phone number, social security, …) being collected?".</p> <p>A full description of the dataset can be found in D3.4 of the SMOOTH project</p>
Contact tracing solutions for COVID-19: applications, data privacy and security :: Suplementary Material
<p>Supplementary Material for the paper "Contact tracing solutions for COVID-19: applications, data privacy and security"</p>
Data for research article "Privacy Explanations – A Means to End-User Trust"
<p>Research data for article "<strong>Privacy Explanations – A Means to End-User Trust</strong>". This package includes the survey and its results.</p>
FedscGen: privacy-aware federated batch effect correction of single-cell RNA sequencing data -- Preprocessed datasets
<div> <div> <div> <div> <p>This dataset accompanies the publication "FedscGen: Privacy-Aware Federated Batch Effect Correction of Single-Cell RNA Sequencing Data" and includes eight single-cell RNA sequencing (scRNA-seq) datasets used to benchmark the FedscGen and scGen methods. The datasets are provided in <code>.h5ad</code> format and include comprehensive metadata necessary for replication and further analysis.</p> <h3>Datasets</h3> <p>We analyze various datasets to compare FedscGen against scGen (centralized) in terms of batch correction. For simplicity, we refer to the dataset by abbreviations:</p> <ol> <li> <p><strong>Cell Line (CL)</strong>:</p> <ul> <li>Derived from the 293t_jurkat experiment with three batches: Zheng et al., 2017.</li> </ul> </li> <li> <p><strong>Human Dendritic Cells (HDC)</strong>:</p> <ul> <li>scRNA-seq data of human dendritic cells across two batches: Villani et al., 2017.</li> </ul> </li> <li> <p><strong>Human Pancreas (HP)</strong>:</p> <ul> <li>Consolidated data from five sources with 14,767 cells each: Baron et al., 2016; Muraro et al., 2016; Segerstolpe et al., 2016; Wang et al., 2016; Xin et al., 2016.</li> </ul> </li> <li> <p><strong>Mouse Brain (MB)</strong>:</p> <ul> <li>Merged datasets with 691,600 and 141,606 cells: Saunders et al., 2018; Rosenberg et al., 2018.</li> </ul> </li> <li> <p><strong>Mouse Cell Atlas (MCA)</strong>:</p> <ul> <li>Data focusing on 11 cell types from various organs: Han et al., 2018; The Tabula Muris Consortium, 2018.</li> </ul> </li> <li> <p><strong>Mouse Hematopoietic Stem and Progenitor Cells (MHSPC)</strong>:</p> <ul> <li>Data from SMART-seq2 and MARS-seq protocols: Nestorowa et al., 2016; Paul et al., 2015.</li> </ul> </li> <li> <p><strong>Mouse Retina (MR)</strong>:</p> <ul> <li>Data from two unassociated laboratories with 26,830 and 44,808 cells: Macosko et al., 2015; Shekhar et al., 2016.</li> </ul> </li> <li> <p><strong>PBMC (human Peripheral Blood Mononuclear Cell)</strong>:</p> <ul> <li>scRNA-seq data with two batches: Zheng et al., 2017.</li> </ul> </li> </ol> <p><strong>Usage Notes</strong>: Each dataset is provided in <code>.h5ad</code> format, compatible with common single-cell analysis tools such as Scanpy. Detailed metadata is included within each file.</p> <p><strong>Keywords</strong>: Single-cell RNA sequencing, scRNA-seq, Batch effect correction, Privacy-aware, Federated learning, scGen, FedscGen, Clinical multi-center studies, Genomics, Bioinformatics</p> <p><strong>Contact</strong>: For questions or further information, please contact Mohammad Bakhtiari at <a href="mailto:mohammad.bakhtiari@uni-hamburg.de.">mohammad.bakhtiari@uni-hamburg.de.</a></p> <p><strong>License</strong>: Creative Commons Attribution 4.0 International (CC BY 4.0)</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
DATA FOR STUDY OF PRIVACY ATTITUDE OF USERS OF SOCIAL NETWORKING SITES AND THEIR EXPECTATIONS FROM LAW IN INDIA
<p>In the present study, on Indian population a disproportionate, stratified, purposive, convenience mixed sampling technique has been adopted in order to ensure proper representation of all the stakeholders concerned with regard to the issue of data privacy in India among the population of the study. The stratified sampling technique is popularly used for a large size population and where it is desirable to purposively have an adequate representation of the all the sub-groups. It is estimated that there are about 0.2 million Law Enforcement Officers (Directors General of Police to Assistant Sub- Inspector), 2.2 million Legal Professionals, including 21,586 Judges, 1.5 million Academicians, about 5000 Information Assurance and Privacy Experts, and 450 million Internet Users in India, out of which 196 million use SNSs. It may be clarified here that all these stakeholders are not only the users of social networking sites but also Indian citizens who are stakeholders in enactment and implementation of the privacy law as and when it is enacted.</p> <p>Based on the size of the total population, a statistically adequate sample size of 385, having a 95 per cent Confidence Level, 5 per cent Margin of Error (Confidence Interval), 0.5 Standard Deviation, a 1.96 Z-score was calculated.</p> <p> </p> <p> </p> <p> </p>
Data Privacy Vocabulary (DPV)
<h1>Data Privacy Vocabulary (DPV)</h1> <p>The <a href="https://w3id.org/dpv" rel="nofollow">Data Privacy Vocabulary (DPV)</a> provides an ontology (classes and properties) and taxonomies of concepts to represent information regarding how personal data is processed in the form of an ontology or a knowledge graph. For example, it provides taxonomies associated with:</p> <ul> <li>purposes of processing</li> <li>personal data categories involved</li> <li>processing operations</li> <li>technical and organisational measures or restrictions applied</li> <li>legal basis used to justify processing</li> <li>information about legal basis for processing</li> <li>rights as applicable</li> <li>risks as applicable</li> </ul> <p>The namespace for DPV terms is <code>http://w3id.org/dpv#</code> with suggested prefix <code>dpv</code>, and serialisations are provided in RDF/XML, Turtle, JSON-LD, and N3 formats. The default serialisations are defined using RDFS/SKOS semantics, with an <a href="https://w3id.org/dpv/dpv-owl" rel="nofollow">alternate serialisation</a> defined using OWL2 semantics.</p> <div> <h2>Extensions</h2> <a href="https://github.com/w3c/dpv#extensions"></a>These extensions provide additional concepts that extend the concepts and scope of the main DPV specification:</div> <ul> <li><a href="https://w3id.org/dpv/pd" rel="nofollow">Personal Data (PD)</a> provides a taxonomy of personal data categories</li> <li><a href="https://w3id.org/dpv/loc" rel="nofollow">Location (LOC)</a> provides a taxonomy of location concepts based on ISO 3166 (countries, regions)</li> <li><a href="https://w3id.org/dpv/tech" rel="nofollow">Technology (TECH)</a> provides a taxonomy of technology concepts</li> <li><a href="https://w3id.org/dpv/ai" rel="nofollow">AI</a> provides a taxonomy of AI concepts extending the TECH extension</li> <li><a href="https://w3id.org/dpv/justifications" rel="nofollow">Justifications</a> provides concepts for representing justifications i.e. why something must be done or could not be done</li> <li><a href="https://w3id.org/dpv/risk" rel="nofollow">Risk</a> provides concepts for risk assessment and management</li> </ul> <div> <h2>Extensions for Jurisdictions and Regulations</h2> <a href="https://github.com/w3c/dpv#extensions-for-jurisdictions-and-regulations"></a>The legal extensions provide concepts associated with specific jurisdictions and the laws, authorities, and treaties within them. The <a href="https://w3id.org/dpv/legal" rel="nofollow">Legal</a> page provides an overview of these. The jurisdictions are represented by using their ISO 3166-2 codes.</div> <ul> <li><a href="https://w3id.org/dpv/legal/eu" rel="nofollow">European Union (EU)</a> <ul> <li><a href="https://w3id.org/dpv/legal/eu/gdpr" rel="nofollow">GDPR</a></li> <li><a href="https://w3id.org/dpv/legal/eu/dga" rel="nofollow">DGA</a></li> <li><a href="https://w3id.org/dpv/legal/eu/aiact" rel="nofollow">AI Act</a></li> <li><a href="https://w3id.org/dpv/legal/eu/rights" rel="nofollow">Charter of Fundamental Rights</a></li> </ul> </li> <li><a href="https://w3id.org/dpv/legal/de" rel="nofollow">Germany (DE)</a></li> <li><a href="https://w3id.org/dpv/legal/ie" rel="nofollow">Ireland (IE)</a></li> <li><a href="https://w3id.org/dpv/legal/in" rel="nofollow">India (IN)</a></li> <li><a href="https://w3id.org/dpv/legal/gb" rel="nofollow">United Kingdom (GB)</a></li> <li><a href="https://w3id.org/dpv/legal/usa" rel="nofollow">United States of America (USA)</a></li> </ul> <div> <h2>Acknowledgements and Citation</h2> <ul> <li>For use of DPV from v2 onwards, <strong>Cite as:</strong> <a href="https://arxiv.org/abs/2404.13426" rel="nofollow">Data Privacy Vocabulary (DPV) -- Version 2</a> by Harshvardhan J. Pandit, Beatriz Esteves, Georg P. Krog, Paul Ryan, Delaram Golpayegani, Julian Flake <a href="https://arxiv.org/abs/2404.13426" rel="nofollow">https://arxiv.org/abs/2404.13426</a> (2024)</li> </ul> </div> <ul> <li>For use of DPV up to v1 and v1.1, <strong>Cite as:</strong> The peer-reviewed article “<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-33246-4_44" rel="nofollow">Creating A Vocabulary for Data Privacy</a>” presents a historical overview of the DPVCG, and describes the methodology and structure of the DPV along with describing its creation. An open-access version can be accessed <a href="http://hdl.handle.net/2262/91581" rel="nofollow">here</a>, <a href="http://doras.dcu.ie/23801/" rel="nofollow">here</a>, and <a href="https://aic.ai.wu.ac.at/~polleres/publications/pand-etal-2019ODBASE.pdf" rel="nofollow">here</a>.</li> </ul>
Supplementary Material for "Investigating Software Development Teams Members' Perceptions of Data Privacy in the Use of Large Language Models (LLMs)"
<h3>ABSTRACT<strong>: </strong></h3> <p><strong>Context</strong>: Large Language Models (LLMs) have revolutionized natural language generation and understanding. However, they raise significant data privacy concerns, especially when sensitive data is processed and stored by third parties. <br><strong>Goal</strong>: This paper investigates the perception of software development teams members regarding data privacy when using LLMs in their professional activities. Additionally, we examine the challenges faced and the practices adopted by these practitioners. <br><strong>Method</strong>: We conducted a survey with 78 ICT practitioners from five regions of the country. <br><strong>Results</strong>: Software development teams members have basic knowledge about data privacy and LGPD, but most have never received formal training on LLMs and possess only basic knowledge about them. Their main concerns include the leakage of sensitive data and the misuse of personal data. To mitigate risks, they avoid using sensitive data and implement anonymization techniques. The primary challenges practitioners face are ensuring transparency in the use of LLMs and minimizing data collection. Software development teams members consider current legislation inadequate for protecting data privacy in the context of LLM use. <br><strong>Conclusions</strong>: The results reveal a need to improve knowledge and practices related to data privacy in the context of LLM use. According to software development teams members, organizations need to invest in training, develop new tools, and adopt more robust policies to protect user data privacy. They advocate for a multifaceted approach that combines education, technology, and regulation to ensure the safe and responsible use of LLMs.</p>
Data from: The dual nature of trust in participatory science: An investigation into data quality and household privacy preferences
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Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition
<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code> author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code> journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code> title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code> year={2025},</code><br><code> volume={74},</code><br><code> number={},</code><br><code> pages={1-20},</code><br><code> keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code> doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 ∼ 96 kHz for non-speech sound and 20 ∼ 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8∼96 kHz) and inaudible (20∼96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>
Conception and Design of Privacy-preserving Software Architecture Templates - Study Data & Questionnaires
<p>Resulting study data and used questionnaires of the expert-interview in the evaluation of privacy templates. This is part of the bachelor's thesis of Nikolai Prjanikov on the topic of "Conception and Design of Privacy-preserving Software Architecture Templates". </p>
Demo data and models for: Automated speech detection in eco-acoustic data enables privacy protection and human disturbance quantification
<p>Folder containing a <strong>demo dataset</strong> and the <strong>model weights</strong> resulting from the ecoVAD pipeline. The data contained in this folder allows for full reproducibility of the pipeline described on the <a href="https://github.com/NINAnor/ecoVAD">ecoVAD GitHub repository</a>.</p> <p>If you have any questions or issues with the dataset, please open an issue on the ecoVAD GitHub repository.</p>
Raw data_Effect of Security_Privacy and Customer Satisfaction on E Commerce Consumer Trust
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Do privacy assurances work? A study of truthfulness in healthcare history data collection
<p>Patients often provide untruthful information about their health to avoid embarrassment, evade treatment, or prevent financial loss. Privacy disclosures (e.g. HIPAA) intended to dissuade privacy concerns may actually increase patient lying. We used new mouse tracking-based technology to detect lies through mouse movement (distance and time to response) and patient answer adjustment in an online controlled study of 611 potential patients, randomly assigned to one of six treatments. Treatments differed in the notices patients received before health information was requested, including notices about privacy, benefits of truthful disclosure, and risks of inaccurate disclosure. Increased time or distance of device mouse movement and greater adjustment of answers indicate less truthfulness. Mouse tracking revealed a significant overall effect (p < 0.001) by treatment on the time to reach their final choice. The control took the least time indicating greater truthfulness and the privacy + risk group took the longest indicating the least truthfulness. Privacy, risk, and benefit disclosure statements led to greater lying. These differences were moderated by gender. Mouse tracking results largely confirmed the answer adjustment lie detection method with an overall treatment effect (p < .0001) and gender differences (p < .0001) on truthfulness. Privacy notices led to decreased patient honesty. Privacy notices should perhaps be administered well before personal health disclosure is requested to minimize patient untruthfulness. Mouse tracking and answer adjustment appear to be healthcare lie-detection methods to enhance optimal diagnosis and treatment.</p>
Data from: Attacks on genetic privacy via uploads to genealogical databases
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Do privacy assurances work? A study of truthfulness in healthcare history data collection
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Extended data for Differential privacy in the 2020 US census, what will it do? Quantifying the accuracy/privacy tradeoff
<p>Full table of summary counts, errors, and empirical privacy loss for a range of levels of geographic hierarchy and stratification, varying epsilon (sup_table) or sample size (sub_table_2)</p>
Data Observasi Analisis Privacy Policy Aplikasi E-Government Tingkat Daerah
<p>Dataset ini bertindak sebagai hasil seluruh observasi mengenai <strong>Analisis Kepatuhan Keamanan Aplikasi <em>E-Government</em> Tingkat Daerah Sebagai Penunjang <em>New Normal</em></strong>. Terdapat dua data yang tersajikan, yakni:</p> <ul> <li>Data kategorisasi permintaan akses pada Aplikasi Pemerintah Daerah. Data ini berisikan <strong>jenis akses</strong> dan <strong>level perlindungan izin </strong>yang dimiliki pada aplikasi <em>e-government </em>tingkat daerah</li> <li>Data pribadi yang diminta pada sampel Aplikasi Pemerintah Daerah. Data ini berisikan deskripsi data pribadi seperti nama, tempat tanggal lahir, password yang diinputkan pada aplikasi <em>e-government </em>tingkat daerah</li> </ul>
The survey data of patients, prescribers, and pharmacists' feedback on ePrescription systems security and privacy
<p><b>Objective: </b>To evaluate the attitudes of the parties (i.e. patients, prescribers, and pharmacists) involved in the ePrescription systems toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing and dispensing<span><span> medication. </span></span></p> <p><b>Methods: </b>The survey contains questions about the features introduced in the proposed approach of using blockcahin and ML in the ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system.</p> <p>The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. We removed the responses of participants who did not complete the surevy questions.</p> <p><b>Conclusion: </b>Our survey showed that a vast majority of respondents in all groups had positive attitudes towards using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.</p>
The survey data of patients, prescribers, and pharmacists' feedback on ePrescription systems security and privacy
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Towards practical privacy-preserving computation of clinical data
<p>Clinical data sets and implementation for the submission "Towards practical privacy-preserving computation of clinical data"</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.