Package: pii-codex
PII Detection, Categorization, and Severity AssessmentAuthor: Eidan Rosado -@EdyVisionAffiliation: Nova Southeastern University, College of Computing and EngineeringProject BackgroundThePII Codexproject was built as a core part of an ongoing research effort in Personal Identifiable Information (PII) detection and risk assessment (to be publicly released later in 2023). There was a need to not only detect PII in text, but also identify its severity, associated categorizations in cybersecurity research and policy documentation, and provide a way for others in similar research efforts to reproduce or extend the research. PII Codex is a combination of systematic research, conceptual frameworks, third-party open source software, and cloud service provider integrations. The categorizations are directly influenced by the research of Milne et al. (2016) while the ranking is a result of category severities on the scale provided by Schwartz and Solove (2012) from Non-Identifiable, Semi-Identifiable, and Identifiable.The outputs of the primary PII Codex analysis and adapter functions are AnalysisResult or AnalysisResultSet objects that will provide a listing of detections, severities, mean risk scores for each string processed, and summary statistics on the analysis made. The final outputs do not contain the original texts but instead will provide where to find the detections should the end-user care for this information in their analysis.Statement of NeedThe general knowledge base of identifiable data, the usage restrictions of this data, and the associated policies surrounding it have shifted drastically over the years. The tech industry has had to adjust to many policy changes regarding the tracking of individuals, the usage of data from online profiles and platforms, and the right to be forgotten entirely from a service or platform (GDPR). While the shift has provided data protections around the globe, the majority of technology users continue to have little to no control over their personal information with third-party data consumers (Trepte, 2020).Understanding if identifiable data types exist in a data set can prevent accidental sharing of such data by allowing its detection in the first place and, in the case of this software package, present sanitized strings, the reasons to why the token was considered to be PII, and permit for the results to be publishable.Potential UsagesPotential usages include sanitizing of dataset strings (e.g. a collection of social media posts), presenting results to users for software examining their interactions (e.g. UX research on user-awareness in cybersecurity applications), etc.Running Locally with PoetryThis project uses Poetry. To run this project, installpoetryand proceed to follow the instructions under/docs/LOCAL_SETUP.md.Note: This project has only been tested with Ubuntu and MacOS and with Python versions 3.9 and 3.10. You may need to upgrade pip ahead of installation.Installing with PIPVideo capture of install provided in LOCAL_SETUP.md file. Make sure you set up a virtual environment with either python 3.9 or 3.10 and upgrade pip with:pipinstall--upgradepip
pipinstall-Upipsetuptoolswheel# only needed if you haven't already done soBefore addingpii-codexon your project, download the spaCyen_core_web_lgmodel:pipinstall-Uspacy
python3-mspacydownloaden_core_web_lgFor more details on spaCy installation and usage, refer to theirdocs.The repository releases are hosted on PyPi and can be installed with:pipinstallpii-codex
pipinstall"pii-codex[detections]"Note: The extras installed with pii-codex[detections] are the spaCy, Micrisoft Presidio Analyzer, and Microsoft Anonymzer packages.Using Poetry:poetryupdate
poetryaddpii-codex
poetryinstallpii-codex--extras="detections"For those using Google Collab, check out the example notebook:UsageVideo capture of usage provided in LOCAL_SETUP.md.Sample Input / OutputThe built-in analyzer uses Microsoft Presidio. Feed in a collection of strings with analyze_collection() or just a single string with analyze_item(). Those analyzing a collection of strings will also be provided with statistics calculated on the risk scores for detected items.frompii_codex.services.analysis_serviceimportPIIAnalysisServicePIIAnalysisService().analyze_collection(texts=["your collection of strings"],language_code="en",collection_name="Data Set Label",# Optional Labelingcollection_type="SAMPLE"# Defaults to POPULATION, used stats calculations)You can also pass in adataparam (dataframe) instead of simple text array with a text column and a metadata column to be analyzed for those analyzing social media posts. Current metadata supported areURL,LOCATION, andSCREEN_NAME.Sample output (results object converted todictfrom notebook):{
    "collection_name": "PII Collection 1",
    "collection_type": "POPULATION",
    "analyses": [
        {
            "analysis": [
                {
                    "pii_type_detected": "PERSON",
                    "risk_level": 3,
                    "risk_level_definition": "Identifiable",
                    "cluster_membership_type": "Financial Information",
                    "hipaa_category": "Protected Health Information",
                    "dhs_category": "Linkable",
                    "nist_category": "Directly PII",
                    "entity_type": "PERSON",
                    "score": 0.85,
                    "start": 21,
                    "end": 24,
                }
            ],
            "index": 0,
            "risk_score_mean": 3,
            "sanitized_text: "Hi! My name is <REDACTED>",
        },
        ...
    ],
    "detection_count": 5,
    "risk_scores": [3, 2.6666666666666665, 1, 2, 1],
    "risk_score_mean": 1.9333333333333333,
    "risk_score_mode": 1,
    "risk_score_median": 2,
    "risk_score_standard_deviation": 0.8273115763993905,
    "risk_score_variance": 0.6844444444444444,
    "detected_pii_types": {
        "LOCATION",
        "EMAIL_ADDRESS",
        "URL",
        "PHONE_NUMBER",
        "PERSON",
    },
    "detected_pii_type_frequencies": {
        "PERSON": 1,
        "EMAIL_ADDRESS": 1,
        "PHONE_NUMBER": 1,
        "URL": 1,
        "LOCATION": 1,
    },
}DocsFor more information on usage, check out the respective documentation for guidance on using PII-Codex.TopicDocumentDescriptionPII Type MappingsPII MappingsOverview of how to perform mappings between PII types and how to review store PII types.PII Detections and AnalysisPII Detection and AnalysisOverview of how to detect and analyze stringsLocal Repo SetupLocal Repo SetupInstructions for local repository setupExample AnalysisExample Analysis NotebookNotebook with example analysis using MSFT PresidioPII-Codex Docsdocs/pii_codex/index.htmlAutogenerated docs on classes, services, and modelsAttributionsThis project benefited greatly from a number of PII research works like that from Milne et al (2016) with the definition of the types and categories, Schwartz and Solove (2012) with the severity levels of Non-Identifiable, Semi-Identifiable, and Identifiable, and the documentation by NIST, DHS (2012), and HIPAA (full list of foundational publications provided below). A special thanks to all the open source projects, and frameworks that made the setup and structuring of this project much easier like Poetry, Microsoft Presidio, spaCy (2017), Jupyter, and several others.Foundational PublicationsThe following publications that inspired and provided a foundation for this repository:ConceptDocumentDescriptionPII Type MappingsMilne et al., (2016)PII token categories and NIST and DHS categorizations.Risk ContinuumSchwartz & Solove, (2011)Risk continuum concept and definition (what lead to the ranking in PII-Codex).Privacy and AffordancesTrepte, (2020)Third-Party data consumption and user control (lack thereof) background.Social Media and PrivacyBeigi & Liu, (2010)Privacy issues with social media and third-party data consumption.Privacy Settings and Data AccessMoura & Serrão, (2016)Privacy settings, data access, and unauthorized usage.Information Privacy ReviewBélanger & Crossler, (2011)Concept of aggregation of data to identify individuals.Big Data and Third Party Data ConsumptionTene & Polonetsky, (2013)Third-party data usage, user control, and privacy.PII and ConfidentialityMcCallister et al., (2010)NIST guidance on PII confidentiality protections for federal agencies.Data Capitalism and PrivacyWest, (2017)Data capitalism, surveillance, and privacy.The remaining resources such as python library citations, cloud service provider docs, and cybersecurity guidelines are included in the paper.bib file.Community GuidelinesFor community guidelines and contribution instructions, please view theCONTRIBUTING.mdfile.