In the publication [1] we implemented anonymization and synthetization techniques for a structured data set, which was collected during the HiGHmed Use Case Cardiology study [2]. We employed the data anonymization tool ARX [3] and the data synthetization framework ASyH [4] individually and in combination. We evaluated the utility and shortcomings of the different approaches by statistical analyses and privacy risk assessments. Data utility was assessed by computing two heart failure risk scores (Barcelona BioHF [5] and MAGGIC [6]) on the protected data sets. We observed only minimal deviations to scores from the original data set. Additionally, we performed a re-identification risk analysis and found only minor residual risks for common types of privacy threats. We could demonstrate that anonymization and synthetization methods protect privacy while retaining data utility for heart failure risk assessment. Both approaches and a combination thereof introduce only minimal deviations from the original data set over all features. While data synthesis techniques produce any number of new records, data anonymization techniques offer more formal privacy guarantees. Consequently, data synthesis on anonymized data further enhances privacy protection with little impacting data utility. We hereby share all generated data sets with the scientific community through a use and access agreement. [1] Johann TI, Otte K, Prasser F, Dieterich C: Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics. Eur Heart J 2024;. doi://10.1093/ehjdh/ztae083 [2] Sommer KK, Amr A, Bavendiek, Beierle F, Brunecker P, Dathe H et al. Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores. Life (Basel) 2022;12:749. [3] Prasser F, Eicher J, Spengler H, Bild R, Kuhn KA. Flexible data anonymization using ARX—current status and challenges ahead. Softw Pract Exper 2020;50:1277–1304. [4] Johann TI, Wilhelmi H. ASyH—anonymous synthesizer for health data, GitHub, 2023. Available at: https://github.com/dieterich-lab/ASyH. [5] Lupón J, de Antonio M, Vila J, Peñafiel J, Galán A, Zamora E, et al. Development of a novel heart failure risk tool: the Barcelona bio-heart failure risk calculator (BCN Bio-HF calculator). PLoS One 2014;9:e85466. [6] Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 2013;34:1404–1413.

To access the "Main Study Analysis.omv" anonymised data set created in jamovi™ version 2.3.28,Install the latest solid version of jamovi™ via this link https://www.jamovi.org/download.html.solid version is Recommended for Most Users and the current version has the Latest Features.Download the anonymised data set (.omv), and open via jamovi™, to view it.

One of the key impacts of AMI technology is the availability of interval energy usage data, which can support the development of new products and services and to enable the market to deliver greater value to customers. Requestors can now access anonymized interval energy usage data in 30 minute intervals for all zip codes where AMI meters have been deployed.

We evaluate an experimental program in which the French public employment service anonymized résumés for firms that were hiring. Firms were free to participate or not; participating firms were then randomly assigned to receive either anonymous résumés or name-bearing ones. We find that participating firms become less likely to interview and hire minority candidates when receiving anonymous résumés. We show how these unexpected results can be explained by the self-selection of firms into the program and by the fact that anonymization prevents the attenuation of negative signals when the candidate belongs to a minority. (JEL J15, J68, J71)

READMEAwareness Attribution Studies (Studies 1, 2, and 3)Institution: University of SheffieldOverviewThis archive contains fully anonymised datasets from three studies conducted for the development and validation of the Awareness Attribution Scale.All personal and identifying data have been removed. The data is therefore fully anonymous and not subject to GDPR.ContentsS1 Ease of understanding ANONYMISED DATAData from Study 1, where participants rated the ease of understanding of three alternative items for each of 14 constructs. Values represent ease-of-understanding ratings (Likert scale 1=easiest to understand, 3=hardest to understand).S2 Reliability ANONYMISED DATAData from Study 2, where participants rated 42 items (3 per construct) to assess internal consistency and item reliability. Values represent awareness attribution ratings (Likert scale 1=least aware, 5=most aware).S3 Validation ANONYMISED DATAData from Study 3, where participants rated different entities (rock, robot, dog, human) using the final 14-item scale. Values represent awareness attribution ratings (Likert scale 1=least aware, 5=most aware).Research supported by the European Union under the European Innovation Council (EIC) research and innovation program, Project CAVAA (project number 101071178) as well as Project “VaLue-aware AI (VALAWAI)” (project number 101070930); and by the Royal Society. Ethical approval was obtained by the School of Computer Science Ethics Committee at the University of Sheffield (Reference Number 064307).

Description This dataset contains network traffic and vulnerability scan reports for networks with different characteristics: vlan11 is a public network with low traffic and ~30 hosts cloud is a public network with moderate traffic and ~100 hosts from a cloud environment vlan23 is a private network with high traffic and ~200 hosts Data formats netflow data is presented in (CSV, JSON, RAW) formats for 30 day period security scan reports are presented in (CSV, filtered CSV, HTML, XML) formats Data is compressed in may cases for preserving repository space and network bandwidth. Uncompress with xz Anonymization The anonymized dataset comprises a collection of network traffic and domain-related information derived from the described environments. The source information includes sensitive IPv4 addresses and domain hostnames, vital for network analysis, vulnerability assessments, and security research. However, due to the sensitive nature of the data, anonymization is employed to protect personal and organizational privacy. Anonymization Methodology To ensure privacy while retaining the dataset's analytical value, the following anonymization techniques are applied: The main objective is to maintain the utility of network patterns and relationships while masking specific addresses to prevent any form of trace-back to individual devices or networks. IPv4 Address Anonymization Each IPv4 address in the dataset has its first two octets anonymized, using a consistent mapping system that replaces these octets with random, uniquely assigned numbers. This transformation is deterministic, meaning that the same original address segments always map to the same anonymized segments, thus preserving relationships and patterns critical for analysis. Domain Name Anonymization The hostnames within domain names are anonymized by substituting them with a randomly generated string. These new hostnames follow a structured anonymized format: .random.xyz. Similar to IP anonymization, the mapping is consistent across the dataset, ensuring that each original hostname is consistently replaced with the same anonymized version. Privacy Considerations Consistency: The anonymization process employs a reproducible mapping system, ensuring that every occurrence of a unique IP address segment or domain hostname is anonymized identically across the dataset. This consistency allows for meaningful analysis of trends and repeated interactions without exposing raw data. Data Integrity: By focusing the anonymization on specific segments of IP addresses and hostnames, the overall structure of the data remains intact. This integrity is crucial for operations such as network flow analysis and anomaly detection, which rely on the continuity of data patterns. Data Minimization: Alongside anonymizing critical fields, the dataset also undergoes a process of column removal, where non-essential fields that might contain sensitive information are excluded. This further reduces the risk of unintended information exposure.

Vehicle Data Anonymization Platform Market Outlook

According to the latest research, the global Vehicle Data Anonymization Platform market size reached USD 1.34 billion in 2024, driven by the rapid digitalization of automotive ecosystems and increasing regulatory requirements for data privacy. The market is experiencing robust momentum, registering a CAGR of 22.7% from 2025 to 2033. By 2033, the market is forecasted to reach USD 9.09 billion, reflecting the growing imperative for secure, compliant, and privacy-centric data management solutions in the automotive sector. This exceptional growth is largely attributed to the proliferation of connected vehicles, stringent data protection regulations such as GDPR and CCPA, and heightened adoption of advanced analytics and telematics across fleets and mobility services.

The primary growth factor for the Vehicle Data Anonymization Platform market is the exponential increase in data generated by modern vehicles. With the advancement of connected car technologies, vehicles now produce vast volumes of sensitive data, including location, driver behavior, vehicle diagnostics, and telematics. Automakers, fleet operators, and mobility service providers are under mounting pressure to harness this data for business insights while ensuring compliance with data privacy laws. This has created a critical need for robust anonymization platforms that can strip personally identifiable information (PII) and mitigate privacy risks without compromising the utility of the data for analytics and operational purposes. The integration of artificial intelligence and machine learning in these platforms further enhances their ability to anonymize complex datasets at scale, accelerating adoption across the automotive value chain.

Another significant driver is the global regulatory landscape governing automotive data privacy. Regulatory bodies in North America, Europe, and Asia Pacific have enacted stringent data protection frameworks that mandate anonymization of data before any processing, storage, or sharing. For instance, the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States impose hefty penalties for non-compliance, compelling OEMs, insurers, and fleet operators to invest in advanced anonymization solutions. These regulations are not only fostering compliance but also building consumer trust, as end-users become increasingly aware of their digital rights and demand greater transparency in data handling practices. As a result, the market for vehicle data anonymization platforms is witnessing accelerated investments and innovation, particularly in regions with mature regulatory environments.

The surge in demand for data-driven services—such as predictive maintenance, usage-based insurance, and personalized mobility solutions—is also fueling market growth. Automotive OEMs and service providers are leveraging anonymized data to develop new revenue streams, enhance customer experiences, and optimize operational efficiency. The ability to securely share anonymized vehicle data with third-party partners, such as insurance companies and smart city planners, is unlocking significant value while maintaining regulatory compliance. Additionally, the rise of electric vehicles (EVs) and autonomous vehicles is amplifying the volume and complexity of data generated, further underscoring the necessity of advanced anonymization platforms to safeguard user privacy and support the evolving mobility ecosystem.

Regionally, the market demonstrates strong growth in Europe and North America due to early regulatory adoption and high penetration of connected vehicles. Asia Pacific, however, is emerging as a lucrative market, propelled by rapid urbanization, expanding automotive production, and increasing investments in smart mobility infrastructure. Key economies such as China, Japan, and South Korea are aggressively embracing digital transformation in automotive, resulting in heightened demand for data privacy solutions. Latin America and the Middle East & Africa are gradually catching up, driven by evolving regulatory frameworks and growing awareness of data privacy issues. Overall, the global vehicle data anonymization platform market is poised for sustained expansion, supported by technological advancements, regulatory mandates, and the relentless pursuit of data-driven innovation in the automotive sector.

Component Analysis

The Vehicle Data Anonymiza

The file contains anonymized data on mandibular molar evaluations, including patient demographics, tooth presence, root and canal details, and measurements like the distance to the inferior alveolar nerve canal. (XLSX)

Data set for "Handling Environmental Uncertainty in Design Time Access Control Analysis".

Non-anonymous data and code for the SOGOOD-trial

The data package consists of various files that contain different types of information, mainly focusing on anonymous swingers’ data in various regions:

1. Residents Data (tabular)

Focus: Demographic and socio-economic data at the county level, focusing on the swinger community. It includes median ages, populationdensities, and economic factors.Unique Aspects: Inclusion of demographic details like age groups, employment sectors, and divorce rates, allowing for a deeper socio-economicanalysis.Format: The data are provided in both *.xlsx and *.sav formats, allowing sharing and long-term access to the data.

1. Software

Python scripts used for data conversion and structuring are provided for transparency reasons.

1. Calculation Results Files

Files related to various calculations which had led to the specific design of the data are provided for transparency reasons. They are provided in*.xlsx, *.pdf, and *md format, as is most convenient to adequately reflext the respective content.

Please refer to the file readme.md for more details.

This dataset was used in a data science interview and contains anonymized, mixed-type features across numeric, categorical, and date-based columns. The challenge is to build a model that predicts the target variable labeled C, a binary classification label (0 or 1).

With 22 features (F1 to F22), including floating-point values, integers, and dates, this dataset is excellent for experimenting with preprocessing, feature engineering, and binary classification modeling in a realistic setting. The dataset is raw and was originally provided without a business context, making it ideal for assessing general data science skills in an interview-like environment.

Seven raw data files necessary to reproduce hedonic price study in King County, WA. Also included is the cleaned data file (cleanedSales.csv) as well as data dictionary. Visit https://github.com/anonREAuthor/reproducibleRealEstate for data provenance documentation.

BackgroundIdentifying high-risk groups for adverse outcomes after conization is crucial for developing targeted treatment plans for patients with cervical adenocarcinoma in situ (ACIS). This study aimed to analyze the clinical characteristics of patients with ACIS and identify risk factors associated with adverse outcomes.MethodsPatients diagnosed with ACIS through colposcopic biopsy at the Affiliated Hospital of Qingdao University and Qilu Hospital between January 2012 and December 2022 were selected. After meeting the inclusion and exclusion criteria, we collected their clinical data. Chi-square (χ2) tests and logistic regression models were employed to determine independent risk factors.ResultsA total of 379 patients with ACIS were included in this analysis. About 26.1% of these patients tested positive on preoperative endocervical curettage (ECC), while 79.4% had a single lesion. Among the 334 patients who underwent cervical conization, 17.1% had positive surgical margins. Additionally, residual lesions were present in 53.6% of cases, and pathological upgrading occurred in 7.8% of patients. Multivariate analysis indicated that age (p < 0.001), preoperative histopathological results from ECC (p = 0.033), and the number of ACIS lesions (p < 0.001) were associated with positive surgical margins. Number of births (p = 0.011), preoperative histopathological results from ECC (p = 0.030), and surgical margin statuses at cervical conization (p < 0.001) were independent risk factors for residual lesions. Preoperative histopathological result of ECC (p = 0.035) was confirmed as a predictor of postoperative pathological upgrading.ConclusionsOlder, multiparous patients with ACIS and abnormal preoperative ECC results require deeper diagnostic excision. Patients with positive conization margins necessitate further treatment, particularly when accompanied by abnormal ECC results. For women who wish to preserve their fertility, a repeat conization may be appropriate; however, in older and multiparous women, a hysterectomy would be recommended.

This dataset contains the predicted prices of the asset Anonymous over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Context

This data set is a collection of anonymized sample fundraising data sets so that practitioners within our field can practice and share examples using a common data source

Open Call for More Content

If you have any anonymous data that you would like to include here let me know: Michael Pawlus (pawlus@usc.edu)

Acknowledgements

Thanks to everyone who has shared data so far to make this possible.

With Versium REACH's IP to Domain you unlock the ability to de-anonymize your database of IP addresses. Receive firmographic data for an IP address that includes up to 3 likely businesses, including key attributes such as domain, company size, location, and many other valuable firmographic insights.

Anonymised LPIS data for 2023. The following attributes are available 1) LPIS Data: Applicant Herd, Herd Number, Parcel Label, Claimed Area, Crop, Digitised Area, Eligible Hectare, Commonage Denominator, Commonage Numerator, Subdivision, Commonage Indicator, Owner/Leased/Rented, Grassland, Tillage, Permanent, Arable, Straw Incorporation Measure Indicator, Basic Income Support for Sustainability, Eco-Schemes, Complementary Redistributive Income Support for Sustainability, Protein Aid, Complementary income support for young farmers, Areas of Natural Constraints, ACRES, Organic, Straw Incorporation Measure, Manual Deduction Area, Fixed Area Deduction Area, Fixed Area Deduction Description, Manual Deduction Description, Date of Extract 2) LPIS Sub Features Data: Parcel Label, Feature Label, Feature Description, Percentage, Gross Area.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute.

The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021.This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license.

This dataset comprises one spreadsheet with N=91 anonymised survey responses .xslx format. It includes all responses to the project survey which used Google Forms between 06-Feb-2023 and 30-May-2023. The spreadsheet can be opened with Microsoft Excel, Google Sheet, or open-source equivalents.

The survey responses include a random sample of researchers worldwide undertaking qualitative, mixed-methods, or multi-modal research.

The recruitment of respondents was initially purposive, aiming to gather responses from qualitative researchers at research-intensive (targetted Russell Group) Universities. This involved speculative emails and a call for participant on the University of Sheffield ‘Qualitative Open Research Network’ mailing list. As result, the responses include a snowball sample of scholars from elsewhere.

The spreadsheet has two tabs/sheets: one labelled ‘SurveyResponses’ contains the anonymised and tidied set of survey responses; the other, labelled ‘VariableMapping’, sets out each field/column in the ‘SurveyResponses’ tab/sheet against the original survey questions and responses it relates to.

The survey responses tab/sheet includes a field/column labelled ‘RespondentID’ (using randomly generated 16-digit alphanumeric keys) which can be used to connect survey responses to interview participants in the accompanying ‘Fostering cultures of open qualitative research: Dataset 2 – Interview transcripts’ files.

A set of survey questions gathering eligibility criteria detail and consent are not listed with in this dataset, as below. All responses provide in the dataset gained a ‘Yes’ response to all the below questions (with the exception of one question, marked with an asterisk (*) below):

· I am aged 18 or over · I have read the information and consent statement and above. · I understand how to ask questions and/or raise a query or concern about the survey. · I agree to take part in the research and for my responses to be part of an open access dataset. These will be anonymised unless I specifically ask to be named. · I understand that my participation does not create a legally binding agreement or employment relationship with the University of Sheffield · I understand that I can withdraw from the research at any time. · I assign the copyright I hold in materials generated as part of this project to The University of Sheffield. · * I am happy to be contacted after the survey to take part in an interview.

The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk

Postdoctoral Research Assistant Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science

This dataset contains the predicted prices of the asset Anonymous Coin over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.