Data anonymizer

Author: w | 2025-04-24

python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized data-anonymized datasets-preparation datasets-csv python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized

Data Anonymizer: Open Source to Anonymize Personal Data

Data anonymizerData anonymizer allows you to strip information from a dataset while preserving certain statistical properties. This is useful for data companies as it allows them to get access to data that is based on their clients' data without accessing the true data itself. Given that some transforms are non-reversible, the original data cannot be recovered.Getting startedPythonClone the repository.poetry install.You can now use data-anonymizer as long as you are within the created virtual environment.DockerClone the repository.docker build -t data-anonymizer .You can now use data-anonymizer by running docker run --rm -it -v data-anonymizer Exampledata-anonymizer input.csv output.csv--feature-min-max-scale bananas celeries--feature-binarize avocados--feature-categorize entity carrots--feature-remove tomatoes--feature-anonymize carrots celeries--feature-fill bananas 500--feature-clamp avocados 1 3--feature-round beets 0.15An example filedate,entity,bananas,carrots,celeries,tomatoes,avocados,beets2019-01-01,a,1,2,3,11,1,1.02019-02-01,a,4,5,6,15,50,1.32019-03-01,a,7,3,9,17,13,1.462019-01-01,b,1,2,3,5,6,2.672019-02-01,b,4,5,6,7,3,-5.662019-03-01,b,7,8,9,9,26,-1will turn into# feature-min-max-scale bananas 1 # feature-binarize avocados # feature-categorize entity 0 # feature-fill bananas 500 # feature-clamp avocados 1 3 # feature-round beets 0.15 date,entity,bananas,0,1,avocados,beets2019-01-01,C0,500,C0,0.0,1.0,1.052019-02-01,C0,500,C2,0.5,1.0,1.34999999999999992019-03-01,C0,500,C1,1.0,1.0,1.52019-01-01,C1,500,C0,0.0,1.0,2.69999999999999972019-02-01,C1,500,C2,0.5,1.0,-5.72019-03-01,C1,500,C3,1.0,1.0,-1.05At the top of the document data-anonymizer adds metadata on some of the transforms that were applied to the data. Fields that have been anonymizer will be referred with their anonymized name. The metadata does not provide information that can lead to the recovery of the original data, but helps the data scientist working on the anonymized data how the transformed data came to be.LicenseThe code is licensed under the MIT license. See LICENSE.

GitHub - CloudTooling/data-anonymizer: Anonymize connected data

GlossaryAnonymizerWhat Is Anonymizer?Anonymizer is a technology or service designed to mask the identity of users while they engage with the internet. It operates by rerouting internet traffic through various servers and encrypting the data transmitted, rendering the user's online activities virtually untraceable. This ensures that your IP address, location, and other identifying information remain hidden from websites, advertisers, hackers, and even your internet service provider.The Origin of AnonymizerThe concept of anonymizing internet activity can be traced back to the early days of the internet. It was born out of the need to protect users from the inherent risks associated with online interaction. Anonymizer services have evolved over the years to offer more sophisticated methods of concealing user identities, aligning with the ever-growing threats in the digital landscape.A Practical Application of AnonymizerOne of the most prevalent practical applications of Anonymizer is in online security. Individuals can use it to protect their personal information, especially when connecting to public Wi-Fi networks, which are often vulnerable to cyberattacks. Additionally, businesses utilize Anonymizer to safeguard their intellectual property, confidential data, and customer information from potential breaches. By obfuscating user identities and encrypting data, Anonymizer ensures that sensitive information remains private and secure.Benefits of Anonymizer1. Privacy Preservation: Anonymizer is a bulwark against intrusive data collection. By masking your identity, it shields you from the relentless tracking of your online behavior, allowing you to browse without being constantly monitored. 2. Enhanced Security: Anonymizer adds an extra layer of security to your online experience. By encrypting your data, it safeguards against eavesdropping and cyberattacks, making it more challenging for malicious actors to compromise your information. 3. Geographical Freedom: Anonymizer allows you to access geographically restricted content by rerouting your traffic through servers in different locations. This is particularly useful for streaming services and circumventing censorship in some regions. 4. Protection on Public Networks: When using public Wi-Fi, which is often unsecured, Anonymizer keeps your data secure and private, mitigating the risk of potential cyber threats.FAQYes, Anonymizer tools and services are legal in most countries. However, the legality can vary depending on the purpose and the jurisdiction. It's essential to use them for lawful and ethical purposes.No, Anonymizer services can vary in terms of features, security, and performance. It's important to choose a reputable and reliable service that aligns with your specific needs.While Anonymizer significantly enhances your online privacy and security, it cannot offer absolute protection. It's still important to practice safe online habits, such as using strong passwords and being cautious about the information you share.

Details of anonymization layer: data anonymizer

Actions, you can create your own dictionary by creating a json file dictionary.json :{ "(0x0002, 0x0002)": "ActionName", "(0x0003, 0x0003)": "ActionName", "(0x0004, 0x0004)": "ActionName", "(0x0005, 0x0005)": "ActionName"}Same as before, the ActionName has to be defined in the actions list.dicom-anonymizer InputFilePath OutputFilePath --dictionary dictionary.jsonIf you want to use the regexp action in a dictionary:{ "(0x0002, 0x0002)": "ActionName", "(0x0008, 0x0020)": { "action": "regexp", "find": "0701$", "replace": "0000" }}Custom/overrides actionsHere is a small example which keeps all metadata but updates the series descriptionby adding a suffix passed as a parameter.import argparsefrom dicomanonymizer import ALL_TAGS, anonymize, keepdef main(): parser = argparse.ArgumentParser(add_help=True) parser.add_argument( "input", help="Path to the input dicom file or input directory which contains dicom files", ) parser.add_argument( "output", help="Path to the output dicom file or output directory which will contains dicom files", ) args = parser.parse_args() deletePrivateTags = False input_dicom_path = args.input output_dicom_path = args.output extra_anonymization_rules = {} # Per # it is all right to retain only the year part of the birth date for # de-identification purposes. def set_date_to_year(dataset, tag): element = dataset.get(tag) if element is not None: element.value = f"{element.value[:4]}0101" # YYYYMMDD format # ALL_TAGS variable is defined on file dicomfields.py # the 'keep' method is already defined into the dicom-anonymizer # It will overrides the default behaviour for i in ALL_TAGS: extra_anonymization_rules[i] = keep extra_anonymization_rules[(0x0010, 0x0030)] = set_date_to_year # Patient's Birth Date # Launch the anonymization anonymize( input_dicom_path, output_dicom_path, extra_anonymization_rules, delete_private_tags=False, )if __name__ == "__main__": main()See the full application in the examples folder.In your own file, you'll have to define:Your custom functions. Be careful, your functions always have in inputs a dataset and a tagA dictionary which map your functions to a tagAnonymize dicom tags for a datasetYou can also anonymize dicom fields in-place for pydicom's DataSet using anonymize_dataset. See this example:import pydicomfrom dicomanonymizer import anonymize_datasetdef main(): # Create a list of tags object that should contains id, type and value fields = [ { # Replaced by Anonymized 'id': (0x0040, 0xA123), 'type': 'LO', 'value': 'Annie de la Fontaine', }, { # Replaced with empty value 'id': (0x0008, 0x0050), 'type': 'TM', 'value': 'bar', }, { # Deleted 'id': (0x0018, 0x4000), 'type': 'VR', 'value': 'foo', } ] # Create a readable dataset for pydicom data = pydicom.Dataset() # Add each field into the dataset for field in fields: data.add_new(field['id'], field['type'], field['value']) anonymize_dataset(data)if __name__ == "__main__": main()See the full application in the examples folder.For more information about the pydicom's Dataset, please refer. python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized data-anonymized datasets-preparation datasets-csv python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized

Amnesia Anonymization Tool - Data anonymization

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GitHub - joostvunderink/js-data-anonymizer: Anonymize sensitive data

Two packages offers unlimited requests.DB-IPDB-IP are focused on using geolocation to enable the provision of country-specific experiences in an easy to integrate product.DB-IP offers both API and database options. It's a RESTful API using JSON encoding supporting calls either by PHP client library or direct API calls by HTTP or HTTPS. It also offers a dashboard complete with analytics.DB-IP offers one free plan and three paid products. The free version is limited to 1,000 requests per day and its location information stops at city level. Features scale up across the paid packages. Each package is also split into three pricing levels which scale the number of API requests and level of support to serve a wider range of use cases.ipgeolocationipgeolocation's IP Location API has SDKs in Java, C#, Typescript, JavaScript, PHP, and JQuery. It outputs in JSON or XML format, and nine different languages. It also boasts response times of under 40 ms. Geographical location data is detailed including latitude and longitude, ISO 3166-1 compliant country codes, calling code, time, and currency. It also provides anonymizer/proxy detection.The free option can serve up to 1,000 requests per day or 30,000 per month. It also uses HTTPS which most other products reserve for paid packages. All that separates the paid packages is the number of API requests each supports. Should you need more than 50 million per month, there's an enterprise option.ip-infoip-info does a great job on their website at showing how their geolocation API can be used in different sectors. Their use cases span e-commerce, financial services, gaming, and more.It uses 256 bit SSL encryption and is available in 10 different programming languages. It can provide country name and country code, latitude and longitude, and ISP data, alongside anonymizer/proxy detection.The free option handles up to 50,000 API requests per month. The paid options scale the features and the number of monthly API requests. The pricing and package options, such as an account manager to aid implementation, indicate that ip-info is targeting corporate clients.ip location finderip location finder's website is strange, making it difficult to source simple information, while making it easy to understand how to integrate it with a wide range of CDNs. It's a RESTful API available in PHP, C#, Python, and Node.Geolocation information uses IP data to go to latitude and longitude level. Also available is ISP detection, ISP data, anonymizer/proxy detection, and more.Pricing is structured per GB of data up to one of four monthly limits. So you'll need to be clear about how much data you're likely to use.Geolocation APIDeveloped by the W3C internet standards organization, Geolocation API is a free API based on JavaScript binding and requires browser support using HTTPS. As you'd expect from W3C,

GitHub - calebdinsmore/data-anonymizer: A CLI tool that anonymizes data

It is a unique program with unique capabilities, designed and developed in-house by us, from the ground up, with the most advanced programming tools and it is the result of research and development on DICOM imaging of the last two decades. Sante DICOM Viewer Pro is not a "yet another DICOM viewer" constructed with freely available libraries such as dcmtk, itk and vtk, like hundreds of other DICOM viewers which differ from each other only in the appearance, menus, panels and dialog boxes. Sante DICOM Viewer Pro is a professional DICOM viewer, anonymizer, converter, PACS client, mini PACS server, patient CD/DVD burner (with viewer) and much more. Retrieve, view, store, archive, manage and burn medical images Sante DICOM Viewer Pro is a viewer, converter, anonymizer for DICOM files, a DICOM CD/DVD burner, a PACS client/mini server, and much more.​ Is the most known of our products and one of the most popular DICOM viewers worldwide, with thousands of satisfied users. Free Download Sante DICOM Viewer Pro 12.2.5 | 127.5 Mb. python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized data-anonymized datasets-preparation datasets-csv python security data-science cryptography crypto python3 dataset datasets anonymize sensitive sensitive-data data-security data-anonymization anonymizer anonymized