Scott Crouch, Mark43 // Reimagining Police Software

IDENTIFYING DUPLICATE PEOPLE IN LAW ENFORCEMENT RECORDS

mark43

scott crouchco-founder & ceo

ej bensingsoftware engineer

mark43

A LITTLE BIT ABOUT US

founded

mission

help our first responders fight violent crime

funded by

2012

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THE PROBLEM IN LAW ENFORCEMENT SOFTWARE

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HUGE FRAGMENTATION ISSUES

18,000+ U.S. police departments

most software currently on premise

data incompatibility issues

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WHAT WE’RE BUILDING

cloud-based records management, and analysis platform

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A TYPICAL ARREST

domestic violence - aggravated assault

1 suspect, 1 victim

1 gun recovered

avg. # of fields

344

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THE DATA ISSUE

for each person that is arrested

fields collected

85-150

NUMEROUS DUPLICATES OF PEOPLE

no universal master record of people

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IS THERE A FEDERAL SOLUTION?

national warrant/wanted database

NCIC

master fingerprint ID system

IAFIS

all administered by the FBI, but no master person records

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WHAT CAN WE DO ABOUT THIS?

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WORKING WITH THE DATA

Washington, D.C. Metropolitan Police Dept.

20,000,000 reports

5,000,000 people

we had to import

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DIFFICULTIES

names are not unique identifiers

data is very siloed

cannot legally, automatically merge people

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WHAT TO DO?

87.7% of Americans can be correctly identified by DOB, Gender, and Location1

K-Anonymity leads to a Quasi-Identifier2

L. Sweeney, “Simple Demographics Often Identify People Uniquely.” Carnegie Mellon University, Data Privacy Working Paper 3. Pittsburgh, 2000.

L. Sweeney, “k-anonymity, a model for protecting privacy” International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 2002; 557-570.

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OUR APPROACH

quasi-identifiers to create groups of “likely duplicate” records

generic enough to work on other datasets (property, vehicles, etc.)

string matching

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RESULTS

sample data set

accuracy of our first try?

5,000 people

2,000 known duplicates

80% of duplicates correctly identified

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MOVING FORWARD

incorporating

dealing with rollbacks and versioning

real-time recommendations

officer feedbackcorrect and incorrect matched data

handlingpropertyevidencevehicleslocations