Introducing DLP: a tool to help screen for and manage sensitive data 

As part of its role in managing HDX, the Centre is aware of various types of sensitive data collected and used by our partners to meet needs in humanitarian operations. While organisations are not allowed to share personally identifiable information (PII) on HDX, they can share survey or needs assessment data which may (or may not) be sensitive due to the risk of re-identifying people and their locations. The HDX team manually reviews every dataset uploaded to the platform as part of a standard quality assurance (QA) process. As the platform has scaled and the range of data types shared by partners has expanded, screening for and identifying potentially sensitive, high-risk data has become more challenging. 

To improve this process, we have integrated a detection tool from Google called Cloud Data Loss Prevention (DLP) into our screening process. Our goal is to screen all data uploaded to HDX for PII and other non-personal sensitive data. By using DLP, we are able to quickly scan the entire data file for different attributes (e.g. column headers that may indicate the presence of sensitive data) and identify potentially sensitive information. Datasets flagged as sensitive based on our criteria are marked ‘under review’ in the public interface of HDX and made inaccessible until the HDX team completes a manual review of the data. Over time, we will refine our use of DLP based on its performance, adding or removing key attributes to improve the detection of different forms of sensitive data. 

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NB: Google maintains extensive documentation of DLP that serves as a helpful technical resource when getting started. Our documentation supplements and contextualizes that resource by presenting a humanitarian use case of DLP. See “Annex: Navigating the official Google DLP documentation” at the end of this document for some tips on how to find what you’re looking for.

Preparing DLP: identify a use case and plan how to add DLP to your workflow

Google Cloud Data Loss Prevention (DLP) is a service designed to discover, classify, and protect sensitive information. It provides the ability to: 

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The diagram below shows how the Centre has integrated DLP into its standard QA process:

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Customizing DLP: choose and create infoTypes for humanitarian contexts 

Standard infoTypes: built-in detection mechanisms

Cloud DLP provides a set of over 120 built-in information types – known as ‘infoTypes’ – to define the sensitive data it can detect in a resource. There are both global infoTypes and country-specific infoTypes. For example, Location and Gender are global infoTypes, and France_Passport is a country-specific infoType that detects French passport numbers. Google maintains a list of all its built-in infoTypes here. 

The first step in preparing a DLP scan is to choose a relevant set of standard infoTypes for your use case. The Centre currently includes has used the following standard global infoTypes in its scans: AGE, CREDIT_CARD_NUMBER, DATE, DATE_OF_BIRTH, EMAIL_ADDRESS, ETHNIC_GROUP, GENDER, GENERIC_ID, ICD10_CODE, ICD9_CODE, IMEI_HARDWARE_ID, LOCATION, MEDICAL_TERM, ORGANIZATION_NAME, PERSON_NAME, PHONE_NUMBER, STREET_ADDRESS, and CREDIT_CARD_NUMBERURL.  

The Google DLP team controls the standard infoType detectors, meaning they may update them or add new ones periodically. In order to monitor these externally unavailable changes, the Centre recommends that organisations create a benchmark file to test at regular intervals.

Custom infoTypes: personalized detection mechanisms

While standard infoTypes are useful in certain contexts, custom infoTypes allow humanitarian organizations to specify and detect potentially sensitive keywords associated with affected people, humanitarian actors and/or a response. 

Typically, custom infoTypes are dictionaries (e.g. text files containing lists of words or phrases, with each new line treated as its own unit). DLP only matches alphanumeric characters, so all special characters are treated as whitespace. For example, “household size” will match “household size,” “household-size,” and “household_size.” Dictionary words are also case-insensitive.

Custom infoTypes may also be regular expressions, enabling DLP to detect matches based on regular expression patterns.  

The Centre has created and currently uses a set of twelve custom infoTypes:

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Unlike the standard infoTypes, the custom infoTypes must be maintained by your organisation. Some custom infoTypes are essentially complete from the outset; others may require updates over time as your organisation learns and adapts. 

The Centre has organized its custom infoTypes into three main categories, as described in the table below: comprehensive, comprehensive in context, and not comprehensive. 

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Category

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Description

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(1) Comprehensive:

GEO_COOR (regex)

HXL_TAGS (regex)

PROTECTION_GROUP

RELIGIOUS_GROUP

SEXUALITY

SPOKEN_LANGUAGE

Static. No updates needed unless errors or omissions are found. 

Example: SPOKEN_LANGUAGE will not need to be updated unless certain rare or dying languages appear to be missing. 

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(2) Comprehensive in context:

DISABILITY_GROUP

EDUCATION_LEVEL

MARITAL_STATUS

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Functionality will be dependent on the correct context of key terms. 

Example: “single” is not exclusively a marital status, just as “primary” is not always an education level.

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(3) Not comprehensive: 

OCCUPATION

HH_ATTRIBUTES

HDX_HEADERS

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Difficult to capture all possibilities upfront; may need updates as more datasets are scanned. 

Example: “child_headed”, “families headed by children”, and “hohh child” all express the same household attribute; different data contributors may have their own versions.

Over time, we will refine our use of DLP based on its performance. This process will involve  Over time, the Centre will refine its use of DLP by adding, updating, or removing custom infoTypes across these three categories to improve the detection of different forms of sensitive data. 

Testing and Refining DLP: assess the suitability of the results for your use case

Once an organisation has selected standard infoTypes and created custom infoTypes to use in their scans, it is time to test and refine DLP to make sure the output meets their requirements.

Given data input, DLP returns details about 1) the infoTypes detected in the text; 2) a likelihood, ranging from VERY_UNLIKELY to VERY_LIKELY with default POSSIBLE, that indicates how likely it is that the data matches the given infoType; and 3) a quote, which is the actual string of data identified as the infoType.

Because organisations will Over the course of 4 months, the Centre conducted 6 rounds of testing on a set of 70 sensitive datasets and 70 placebo datasets. This testing strategy is not necessarily universal; organisations should focus on the DLP capabilities most relevant to their existing data management process, there is no one-size-fits-all approach to testing. However, we recommend answering two key questions before deploying DLP:

• Can DLP detect all or most types of sensitive data that we encounter in our existing data management process?

• How accurately do the detected infoTypes from a scan match the PII and key variables we are trying to catch? 

Over the course of 4 months, the Centre conducted 6 rounds of testing on a set of 70 sensitive datasets and 70 placebo datasets to assess these questions. While the answers may seem obvious from an initial glance at the infoType descriptions, we found it crucial to observe the DLP’s detection mechanisms in actiondetail. For example, when we used using the LAST_NAME infoType in early stages of testing, we looked at the quotes and realized discovered it was flagging both refugee camp names and along with actual surnames. The infoType was not accurately matching the variable we intended to catch. Because microdata uploaded to HDX frequently contains camp names, we determined decided that the LAST_NAME infoType was not particularly helpful to include in the Centre’s scans. Additionally, we expected the LOCATION infoType to detect GPS coordinates, but found that it did not do so in practice. In other words, DLP did not initially detect a type of sensitive data we were looking for. Because of thisIn response, we adjusted our initial assumptions and created a custom infoType to detect longitude and latitude coordinates.

Your organisation Organisations may ultimately differ from the Centre in your answers to the above questionstheir findings, but these types of contextual observations and decisions in light of the two key questions are what underlie define a robust testing process for DLP. Accordingly, the process should draw upon cross-functional expertise from teams across your organisation, not just the data scientists. At the Centre, the Development team managed the technical details of DLP while both the Data Partnerships and the Data Responsibility teams analyzed the outputs of the scans.

Interpreting and Using the Output of DLP: develop actionable criteria for classifying data as sensitive

Even once an organisation is confident that DLP accurately detects the types of sensitive data present in their context, the output of a DLP scan alone does not determine whether a given dataset is sensitive. Ultimately, each organisation needs to define its their own criteria for interpreting the DLP output (e.g. does the presence of a single instance of an infoType mean a dataset is sensitive?)

Depending on the number of standard and custom infoTypes included in the inspection, the raw output of a DLP scan may comprise anywhere from thousands zero to millions of rows of detected matches. On average, it took our team 48 hours to review the full results of a our test scan (scans, which included 70 sensitive datasets and 70 placebo datasets). While reviewing the results for the scan of a scan for a single dataset would take much less time, this process still proved onerous and was non-conducive to our use case of reaching a binary decision about a dataset’s sensitivity. Based on this difficulty, we proceeded to explore whether we could create a complementary tool or algorithm to classify a dataset as sensitive using the training data generated through DLP testing.

A machine learning approach

To interpret the raw output of a DLP inspection scan, the Centre has developed a robust model that averages the results of a random forest model, a generalized linear model, and a gradient boosting model to predict the possibility that a dataset is sensitive or non-sensitive. We generated the training data for this model from our 6 rounds most recent round of testing on 70 sensitive datasets and 70 placebo datasets. The model uses detected infoTypes, likelihoods, and quotes as the main elements in its analysis. 

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As the model is used to classify more and more datasets, its prediction values should become more and more accurate. If we start to see higher levels of model error – e.g. if the QA officer often disagrees with the model’s classification of sensitivity – we will revisit our DLP testing process, may reevaluate our use of certain standard and custom infoTypes, and will retrain the model accordingly. 

In this way, we will refine our use of DLP over time based on its performance. We will continue to update this document based on what we learn.

Annex: Navigating the official Google DLP documentation 

Google maintains extensive documentation of Cloud DLP, including quickstart guides, references, and code samples. The links below provide a simple way to navigate that documentation and assess which capabilities are relevant in your organization’s context.

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Acknowledgement: The Centre’s work to develop an improved technical infrastructure for the management of sensitive data on HDX was made possible with support from the Directorate-General for European Civil Protection and Humanitarian Aid Operations (DG ECHO). Development of this technical documentation was supported through the United Kingdom Foreign, Commonwealth and Development Office (FCDO)’s COVIDAction programme. 

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