Generating Look-alike Names via Distributed ... - Shuchu Han [PDF]

Generating Look-alike Names via Distributed Representations Abstract—Motivated by applications in login challenge and privacy protection, we consider the problem of large-scale construction of realistic-looking names to serve as aliases for real individuals. We seek these look-alike names to preserve name characteristics like gender, ethnicity, and frequency of occurrence while being unlinkable back to the source individual. We introduce the technique of distributed name embeddings, representing names in a high-dimensional space such that distance between name components reflects the degree of cultural similarity between these strings. We present different approaches to constructing name embeddings, and evaluate their cultural coherence. We demonstrate that name embeddings strongly encode gender and ethnicity, as well as name frequency.

I. I NTRODUCTION Names are important. The names that people carry with them are arguably the strongest single facet of their identity. Names convey cues to people’s gender, ethnicity, and family history. Hyphenated last names suggest possible marital relationships. Names even encode information about age, as social trends alter the popularity of given names. That names serve as people’s primary societal identifier gives them even more power. Privacy requirements often make it undesirable or even illegal to publish people’s names without their express permission. Yet there are often technical contexts where we need names which can be shared to represent things: to serve as placeholders in databases, demonstrations, and scientific studies. In this paper, we consider the problem of constructing realistic-looking names on a large-scale to serve as aliases for real individuals. The task here is more subtle than may appear at first. We might consider randomly assigning names from reference sources such as telephone books, but these are actual names and hence violate privacy concerns. We might consider generating names at random from component first/last name parts. But these fake names will not respect gender, ethnicity, and temporal biases: consider the implausibility of names like “Wei Hernandez” or “Roberto Chen”. When replacing names to anonymize a medical study, dissonance is created when female names are replaced by male ones, and the names of elderly patients aliased by newly coined names. Our interest in generating look-alike names arose through a computer security application: how might an email user who lost their password be able to convince the account provider of their identity as part of account recovery process, or as part of the second-login challenge? We reasoned that the genuine account holder should be able to distinguish the actual contacts they have corresponded with from a background of imitation names (Fig. 1). But this is only effective when the background

Fig. 1: Three contact list based challenges using the technique proposed in this paper. In each challenge only one contact is real.

names are culturally indistinguishable from the contacts, a property which did not hold under naive name generation methods. Otherwise, the correct answer might stand out and hence be easily guessed by an attacker who tries to take control of user accounts. For example, consider an example challenge question asked to a hypothetical user, wendy wong@, given in Table I (left). If background names are generated naively without preserving ethnic properties (right), guessing the correct answer becomes much easier. However, when the generated names preserve ethnic and cultural properties of the real contacts that they replace (middle), the guessing task for attacker remains hard, because the imitation names look very similar to the real contacts. TABLE I: A security challenge question: “pick someone you contacted among the following”. Left: the contact list of a hypothetical user wendy wong@. Middle: a replacement list generated using the technique proposed in this paper (retaining one real contact Charles Wan). Right: a naively generated random replacement list. Real Contacts Angela Chiang Paresh Singh Charles Wan Yuda Lin Lin Wong Tony Kuang Hua Yim

Proposed Challenge Amanda Hsu Nirav Sharma Charles Wan Joko Yu Hua Li David Feng Jie Fung

Naive Challenge John Sander Steve Pignootti Charles Wan Jeff Guibeaux Sam Khilkevich Mary Lopez Ron Clemens

The major contributions of our work are:

•

•

•

Generating realistic replacement names through name embeddings – We propose a new technique of representing the semantics of first/last names through highdimensional distributed name embeddings. By training on millions of email contact lists, our embeddings establish cultural locality among first names, last names, and the linkages between them, as illustrated by examples in Figure II. Through nearest neighbor analysis in embeddings space, we can construct replacement aliases for any given name which preserve this cultural locality. Gender, racial, and frequency preservation through name embeddings – Through computational experiments involving ground truth data from the U.S. Census and Social Security Administration, we show that our name embeddings preserve such properties as gender and racial demographics for popular names and industrial sector for corporate contacts. Even more surprisingly, our embeddings preserve frequency of occurrence, a property that to the best of our knowledge has not been previously recognized in the distributed word embedding community. Establishment of ethnic/gender homophily in email correspondence patterns – Through large-scale analysis of contact lists, we establish that there is greater than expected concentration of names of the same gender and race for all major groupings under study.

B. Data Sources and Preparation In this section, we introduce the datasets that are used in this study, as well as a detailed description about our data preparation process. Data Sources. Datasets employed in our work are: • Contact Lists– This set of data, here after referred to as the contact lists is a proprietary sample of contact lists from 2 million distinct Yahoo email users. The length of each contact list varies from 1 to 21, because longer lists have been truncated. Names in each contact list are ordered by contacting frequency/recency. A much larger set of contacts for around 1 billion Yahoo users are available to us and the results using this larger set is very similar to those from the smaller set. Therefore in this paper we present all our results based on the smaller set. Each entry of a list is a full name but not necessarily a human name. To preserve the privacy of users, the owner associated with each contact list was not available in the data. • Census 1990 [2]– The Census 1990 dataset is a public dataset from US Census website. It records the frequently occurring surnames from US Census 1990. This dataset contains 4,725 popular female names and 1,219 popular male names. • Census 2000 [3] – The Census 2000 dataset is another public dataset from US Census website. It contains the frequently occurring 151,672 surnames from US Census 2000. Associated with each name is a distribution over six categories of races. The races are: White, Black, Asian/Pacific Islander (API), American Indian/Alaskan Native (AIAN), Two or more races (2PRACE), and Hispanics. In this paper we refer to the races and ethnicity interchangeably. The ethnicity distributions of Census 2000 and Contact lists data are given in Table III. Data Preparation. The contact lists data records the social interaction of email users. These contact lists include substantial noise in the name fields. Artifacts include omitted names [4], sometimes marked by an arbitrary string like “zzzzzzzz”, or names that are meaningful but clearly not human, like “Microsoft”, “Facebook”, and “GEICO”. Moreover, many names in contact lists data are partial, with only the first name or the last name present.

II. B UILDING NAME E MBEDDINGS A. Methodology We seek to construct a list of background names that looks very plausibly real, even though they are machine generated. One way to create such a list is to start from real contact names, and replace each one with a look-alike names of the same gender, ethnicity and name frequency. However this approach requires multiple complex components, including reliable ethnicity and gender classifiers. Instead we propose to do aways with these complex steps by deriving the signals directly from a large amount of data, through name embedding. In our approach, each name, first or last, is embedded in high dimensional space as a high dimensional vector using word2vec [1]. Our hypothesis (verified in Section III-C) is that people have a tendency to contact people of the same ethnicity and gender. Consequently, when using the contact lists of millions of users as a text corpus, the resulting embedding of names would capture this tendency by placing names of the same gender and ethnicity close-by in the highdimensional space. For each real name in the contact list, we can then choose a name near it in the high dimensional space. The resulting replacements should have good gender and ethnicity similarity to the original names (Table I (middle)). Furthermore, it turns out that in aggregate, the name frequency of the look-alike names also resemble that of the real names very well.

C. Word2vec Embeddings The word2vec software [5] is an efficient tool to learn the distributed representation of words for large text corpus. It comes with two models: the Continuous Bag-of-Words model (CBOW) and the Skip-Gram (SG) model. The CBOW model predicts the current word based on the context while the SkipGram model does the inverse and maximizes classification of a word based on another word in the same context [6]. We start our analysis by using the cleaned contact lists and the word2vec software [5]. Each contact list is treated as a sentence, and together they form a text corpus. Unless

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gender gray. Overall names of the same gender form mostly contiguous regions, indicating that the embedding correctly capture gender information by placing names of the same gender close-by. Figure 2 (right) is an inset showing a region along the male/female border. We can see that “Ollie”, which is considered a predominantly female name per Census data (2:1 ratio of female/male instances), is placed in the male region, close to the male/female border. Per [9], we found that “Ollie” is more often a male name, and used as a nickname for “Oliver” or “Olivia”. Hence our embedding is correct in placing it near the border. The embedding also correctly placed “Imani” and “Darian”, two names not labelled by the Census data, near the border, but in the female/male regions, respectively. Per [9], “Imani” is a African name of Arabic origin, and can be both female and male, mainly female; “Darian” can also be female and male, but mainly male, and

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otherwise stated, all results in the paper are based on the CBOW model with the default word2vec parameter settings (see Section II-D for comparison of different models and ways of constructing the corpus). The output of word2vec is a dense matrix of dimension 517, 539 × 100, with each unique name represented as a row of the matrix. Embedding Visualization. To understand the landscape of the name embeddings, we visualize the names as a 2D map. We used the stochastic neighborhood embedding [7] to reduce the original 100-dimensional embedding to 2D. We assign

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AUDREY

ESTELLE

TERRANCE

NAN

DEBBI

GAYLE

KATHY

BETSY

DIANE

JOANN

COLLEEN

MARYBETH

SOPHIE

DESMOND

BOBBIE LORENE

KITTY

MAURICE

POLLY

KAY

VICKI PAM

LYNN

MARGIE

CAROL

KATHLEEN

JEANIE

CONNIE LIBBY

SUSIE

DEBBIE

CATHY

PATTY

MARY

CORRINE

MEG

TABLE III: Ethnicity distribution of Census 2000 data and that of intersected names between the contact list and Census 2000 data. The label information comes from Census 2000.

GORAN

SAT

VCTOR RUBN

JULIN

DARIO

CLAUDINE

CHANTAL

FRANCE EVELYNE

JEANCLAUDE

YVES

RPONDRE

MARCEL

MYRIAM

LULU

ROSY

PATY

MARGOT

ALIX

JULIETTE

CINDY

PENNY GALE

GAIL

BARBARA

EVIE

EILEEN

MARYANNE

MADELEINE NADINE

CAMILLE

SACHA

JEANNIE

DORI DIANN

PHYLLIS

DONNA

LINDA

SUSAN

NANCY

MARILYN

GLENNA

CINDI

BETH

DENICE

MARSHA BETTY

CHERYL

JANET

KAREN

GAY

MERCY SEBASTIN

NICOLS HORACIO

RAL

IGNACIO

SANTIAGO

HERNAN PABLO

HERNN

MATAS EZEQUIEL

YAN

ANETA

SANTA

GONZALO

SILVINA

ARIEL

MARIANO

NICO

JING

ALEKSANDRA

BILJANA

MARCIN

NESTOR

ESTEBAN

FLORENCIA

VANINA

MATIAS

PACO

GINETTE

ODETTE

GENEVIVE

LUC

JEANPAUL

DENIS MICHEL

JOELLE

JOCELYNE

JACQUES

HENRI DIMITRI MIREILLE

JACKY

FANNY

LETY

LUPITA

AIME

ANNEMARIE

HELENE

ELISABETH

LISE

PIERRE SERGE

XAVIER REN

NENA

IMELDA RUBI

NAYELI

SELENE

MONTSERRAT

ROCO

MICHELINE

FRANCOIS

ARLETTE

CHRISTIAN

ROSALINDA

ISELA XOCHITL ITZEL

PEPE

PAULINA

ARMAND

EMILE

GEORGES

AXEL

LUPE

CRISTAL

MARICELA

BETY ARIADNA

GABY

RAMN

IVN

ELIE

CLAUDE

VALENTINE

BERENICE TERE

ANGELES

CORINNE

FRANCINE

JOSETTE

CYRIL

LIONEL

JOHANNE

REMY

ANAIS

LETTY

ELVA

ESMERALDA

PERLA

GUADALUPE

MARICARMEN IRVING

SAUL

URIEL

HCTOR

ALEXIA

ESTELLA

LIZETTE

BERTHA

MINERVA

ELIA

OFELIA

EFREN

RAYMUNDO LALO

ISRAEL

JACOBO

TOMAS

AGUNG

MARIJA

TA

AGNIESZKA

FABIAN

VANESA

ROMINA YAMILA

ANALIA

MAXIMILIANO

ESTUDIO

CARO

MATEO

GUILLERMO

NANA

NORMA

ANGELICA

YAZMIN

ARACELI

NOE GENARO

ULISES

MAVIS

DOMINIQUE

ANTOINE BERNARD

HUBERT

RENE

KAYE

FAYE

ANN

JOYCE

NOREEN

DEXTER

CARROLL

MITZI GAYE

TIANA

JADA

RAVEN

KESHIA

NIA

JASMINE

ISAIAH

SHAY

NELL

JANIE

MARTI

LYNNETTE

KIANA

AYANA

KIA

YAEL ANDI ANETTE Census ContactJOSEF lists ANNIKA2000 TALIA LUKAS ELISSA BERNICE NICOLLE PEARL CO each SARI name toELANA a cluster using gender/ethnicity ground truth, SOPHIA ALBERTA LARS SUSANNE ILANA RUDI ETHEL INA Races Count Percentage Count Percentage ERICH CECELIA RISA JENS DARA EDWINA ANTON STEPHAN ILENE MAYA KERSTIN SELINA BRITTA and created the maps using gvmap [8]. KIKI VIOLA White RA 115,167 0.8593 12,837,406 JRGEN 0.7428 JOHANNES KIMBERLEE HAZEL ROZ LORNA SONJA ADAC ELISHA DAVINA BIRGIT ANYA ANDREAS 544,983 Black 5,262 ANJA 0.0393 0.0315 SVEN ROLF FRITZ RINA RACHEAL ZALANDO RENA CHRISTOPH MARKUS ROLAND ADEL STEFANI ALEX DAPHNE DINAH TANJA FRANZ Figure 2 (left) illustrates the landscape of first names. This KATJA KATRIN APIJOVAN 6,100 0.0455 KATHARINA1,323,888 0.0766 JANA LIZZY NITA WOLFGANG CHERRY TOBIAS RIA JOHANN PHOEBE ALEXANDER MARLENA ELLA ALEXA LJ HANS AIAN 268 0.002 19,272 0.0011 SABINE visualization establishes that the embedding places names of JANEEN RENATE GRETA TATJANA DIETER EDEN BELLE AGATHA AGNES ALEXIS SU SUSANNAH BERND NAOMI MAJA ALEXANDRA 2PRace MARTINA131 0.001 7,934 0.0005WERNER CHER PETRA ANGELIKA PENELOPE KELI male NIKIcolor the same gender close-by. Using Census data, we MATTHIAS KATARINA GERHARD JOHAN ANNABELLE RONA NONA CATE MA Hispanics 7,089 BETTINA0.0529 HEIKE 2,548,329 0.1475 LEA THEA RAINER DIRK MONIKA NINA JESSIKA ELKE names femaleRHODA names pink, and names with unknown ILSE orange, AILEEN MAE DAGMAR17,281,812 KLAUS Total IVA 134,017 SERENA DANICA JASMIN ANNABEL HILDE INGE URSULA CARIN

ARIS

VESNA

NOELIA MICAELA JESICA

CP

JOAQUIN

EDGARDO

CRISTIAN

EDITH

REYNA LILIA

ROCIO ROSARIO

ROGELIO

RUDY

CHANEL

TYRA

DARIUS

DEMETRIUS

GENIE DIXIE

JERRI

SALLIE PATSY

LAURI

SHERYL

BEVERLY CAROLYN

JANICE

JUNE

LENORE

JEANINE

MAUREEN

REBA

GINGER

JONI

JEROME

DIANNE

SUZANNE JEANNINE

DOREEN

CORNELIUS

REGGIE

RUTHIE

DEE

TERRIE

VICKIE SHERRY

CHERI

CARYN DEBI

SHARON

SHIRLEY

LEONA ANNETTE

MARGO FERN

THERESE JOANNE CAROLE JANINE

COOKIE

JERI TERRI

MELODY

RANDI TERI

ALYCE

DARLENE LORETTA JACKIE

CHARLENE ANITA

MIMI

LYNNE

MARIANNE

LORRAINE

MARION

NOLA

DANNA

SHERRIE

SHERRI

DEENA JACKI

MARLA

BECKY

DOROTHY SUZI

LYNDA

MARGARET

CHARLOTTE

COLETTE ELISE

BARBRA

RHONDA

TAMMY

DIANNA

DENA

SHERI

CHERIE

MARCY

GWEN SHARRON

DEBORAH SUZY

SALLY

JANE

ROSEMARY CHRISTINE ANNE

JEAN

MARIE

TONI

SHEILA

LUCINDA

ELEANOR HELEN

CLAIRE CAROLINE CATHERINE

LOUISE

GENEVIEVE

KELVIN

TISH

TAMMIE

LANA

GENA

QUINCY

BONNY

MALINDA

LORRI

MARCIE

MARCI

DEANNE

LAUREL

DEBRA

THERESA

ELYSE

FAY HARRIET

JAYNE

EVE

JUSTINE

JULES

MICKI SHELLY

LINDY JODY

LORI SHARI

LAURIE

TRISH

SUZIE

VIKKI

SHARYN

PAULINE

MARGUERITE

LUCILLE

GERARD

CEDRIC

BRICE GILBERT

ROXANNA

MARISELA

ELVIA ARACELY

DULCE

LUCERO

ARTURO

SALVADOR

OCTAVIO

OMAR

JAZMIN SARAI

ALMA

AZUCENA

REYES

ELISEO ISIDRO

GERARDO

ENRIQUE

LIZBETH

IRMA

BLANCA

AMERICA

CRUZ

BENITO JESUS ELOY

RUBEN

GERMAN

MAXI

DEWI BUDI

DIDI

NIKOLA

SAN

MICA

AGUSTINA

CONSTANZA

PATRICIO

AGUS

PT

JELENA

ING LIC

AMA

INDRA

ATTY

NIA

ARQ

SOLANGE

KATIA TELMA

LEDA

YESENIA

YESSICA

LIZETH

ALONDRA

EFRAIN

ARNULFO FIDEL

CRISTOBAL

JAVIER

JUAN

ARIANA

NANCI

GUY

GEORGINA VIRGINIA DOLORES

GLORIA MARTHA

DENISSE YADIRA

IVONNE MIREYA

ROSALBA

HERIBERTO

JAIME

HECTOR

JUSTINA XIMENA

MACARENA

PAZ YANINA

MELISA

JOSI

MARIBEL

ESPERANZA MARGARITA

SUSY

AMADO

ARMANDO

RAUL

RAMIRO

ANIBAL

MELINA

DANI

DESI

KUYA

TA

MARLY

ILIANA MAYRA

MARISOL

CONSUELO

MARILU

RIGOBERTO

EDGAR

MIGUEL

ERNESTO

EMILIO

DAISY

MARLENE

CELESTE ROXY

DIANA

VALENTINA GENESIS

KARLA

JUANA

ESTRELLA NOEMI

ELDA

JOSUE

ALFONSO

OSCAR DOMINGO

LORE

SOLEDAD

ELVIRA

ANGEL

MANUEL ROLANDO

ANDRS

SOFA

JIMENA

BELEN

ANABELLA FLORES

LIVIA

WEN

LAZADA

DEJAN

ROMERO

LUCI

CRIS

YESICA JULIETA

HERNANDEZ EUGENIA

TORRES

BERTA

LOURDES

ERICK

ALONSO

GREGORIO

LUIS

ANDRES

AGUSTN CATALINA

MONICA VERONICA

EMANUELA

ELEONORA

JOSELYN

YESSENIA

MAGGIE

WENDY

JACQUIE

MARIETTA ANGELIQUE

DAMIEN QUENTIN

PARIS

AL

ALESIA KIARA

ELIJAH SADE

Fig. 2: Visualization of the name embedding for the most frequent 5,000 first names from email contact data, showings a 2D projection view of name embedding (left). The pink color represents male names while orange denotes female names. Gray names have unknown gender. The right figure presents a close view along the male-female border, centered around African-American names.

PAK

MAS

KA

N

GOMEZ

RODRIGUEZ DIAZ

GONZALEZ

HILDA TERESITA

FELIX

VICTOR

JOSE

ALEJANDRO ALEJO

ADRIN

MARIELA

ROSALIA

ANABEL

ISMAEL

AURELIO

IVAN

MARIO

ALFREDO

ALEJANDRA AGUSTIN ESTEFANIA

MARIANELA

TATI

ISABELA

CRISTIANA

LING

CHE

SITI

BU

HMAN

PEREZ

EVELINE

MARCELLE

LIANE

WALLACE

KAK

BOY

MILENE

LIN

ANDRA

SOL

KARINE CAMILLA

RIKA

JUSSARA

ELSA MIRIAM

ROSA

AURORA

LAZARO

RAMON

LEONEL

JORGE ALVARO

VIVIANA

SANCHEZ

JULISSA HIRAM

CHRISTIANA

FLIX ELIO

OSVALDO

ALBERTO

CECI

MIRTA

MARIEL

CARMEN DELIA AMALIA

MARCELINO SANTOS CESAR JULIO LEOPOLDO

EUGENIO

BIBIANA LILIANA

MALENA

CORINA

MOISES LINO

VICENTE

CARLOS HUGO

ROQUE GUILLERMINA

GRACIELA EVANGELINA

MARTINEZ GARCIA

EDMUNDO

FAUSTO FRANCISCO

RODOLFO

ANAHI

CECILIA LUCRECIA

LOPEZ

MISAEL

BRAULIO

OSWALDO CSAR

ROBERTO SERGIO

GUSTAVO

JONATAN

MERCEDES

LUNA

MARCO

HUMBERTO

JOS

ANTONIO

FERNANDO

MAURICIO DIEGO

SUSANA

ESTELA

GISELA

MARITA

LISETTE

YANIRA

LESLY

MIRNA

HAYDEE

FLOR

ROXANA JOSU

EDGARD

HOMERO

PEDRO RICARDO EDUARDO

GABRIEL

PAOLA

MATILDE

JOSEFINA

ELIAS

ISAIAS

GILBERTO

RAFAEL LVARO

EMANUEL

VERNICA

MNICA

MARA

LUCILA

MABEL CAROLA

RAFA

BERNARDO

GILDA ROSSANA

BEATRIZ CAROLINA

LORENA

INES

FAMILIA

LISSETTE

REINA

ELBA

ROSITA

ILEANA

STEFANIA

LUCA

IVETTE

MILAGROS

MIRTHA

NILDA

GIULIANA

MARITZA

ZOILA

GLADYS

ZULMA

DORA

LESLIE

DENISE JO

VAL

ROSEMARIE DOLLY

ELAINE

ROBERTA MARCIA

MARNIE

MICHELE

CLAIR

FLO

ROSALIE MARCELLA

RITA

PAULA

BERNADETTE

MARJORIE

ARLENE

ROSE

REGINA

CARLA

SABRINA PATRICIA

LIZ

LYN

RUTH JUDITH

ARLEEN

LORAINE

ENID

VIVIAN PRISCILLA VANESSA

KIT

RAE

CAREN

MADELYN

BRENDA

JEANNETTE

JACQUELINE

ALICIA

SANDRA

BARBIE

SONDRA CHARLA

AUGUSTINE

TAMI

KARI

JODI DARCY

JILL

COLEEN

WILLIE

DERRICK

ANGIE

CANDY

ROXIE

RONDA

TAMMI LORA

MISSY

KATI GRETCHEN

MINDY

JOLENE

DAWN

LORRIE

ROBIN RENEE

KARYN

VALERIE

LEIGH PAMELA ANGELA

CYNTHIA

MADELINE

EVELYN

IRIS

GERALDINE

AMELIA

ANDREA

ERIKA

HEIDI

DEANNA

KWAME

JENNY

JEANETTE

LORIE NANETTE

TERESA MILLIE DORIS

MILDRED

MYRNA

GINA

KATHERINE ELIZABETH

LAURA

WILMA ELSIE

LILLIAN

VICKY

LILA

DI

STUDIO

GIULIA

CAREY

CATIE

MOLLIE LEEANN

HOLLY KRIS

STACY KELLEY KIM

HOLLI

CYNDI

DANETTE

JANEL

TRISHA

JAMIE

HEATHER

TRACY SHELLEY

JULIE TINA

KERRY

CAMI

ANGI

BECKIE

JACQUE SHELLI

KARRIE

DEEDEE

GAYLA BECKI

SHELLIE CARI

CHRISTY

KERRI

CARRIE

TRICIA

KELLY STACEY

DANA LISA

MELINDA

JENNIE

JULI

LESA

DARLA

TORY

LEANN SHAWNA

KRISTI

KARIE

KRISTIE

SHANNON

AMY

TARA

SHAUNA

JANNA

ROXANNE

KERI

KRISTA

KRISTIN

KATIE

ERIN

JEN

JENNIFER

MICHELLE

CHRISTA

KELLIE

KARA

KRISTEN

MOLLY

JENN

KATHRYN REBECCA MELANIE

MELISSA

CHRISSY

MEGAN

CARA

LAUREN EMILY LEAH

KATY

STEPHANIE

JESSIE

SARA

GLENDA JULIA MELBA LARA

FRANCES

JANETTE

LUCY

BEA

FRANCESCA

ALESSIA CHIARA

DAVIDE FEDERICA

VICTORIA

JOANNA

ANNA

YVONNE

LYDIA ROSIE

IRENE

ANNALISA

MATTEO

RICCARDO STEFANO

ANTONELLA

OLIVIA

MYRA

BELINDA ELLE

SYLVIA

KOFI

EVA

ANGELINA

MASSIMO

FRANCESCO

GIUSEPPE GIORGIO

FABRIZIO

WANDA

SUSANNA

JUANITA ATHENA

NELDA GABBY

GRACIE LOLA

ESTHER ARIANNA

MAURIZIO

GIANNI

LUIGI

STELLA

GIANNA

SALVATORE VINCENZO

PAOLO

GIOVANNI GIOVANNA NYDIA

YOLANDA

CARISSA MARISSA

DESIREE KASSANDRA

DOMINICK

LILLY ROCCO CARMINE

ENRICO

ENZO FIORELLA

ZAIDA MIGDALIA

DAMARIS

THELMA

NELLIE

CECE

CRISTY VINNIE

VINNY

VITO

ANGELO

CARLO

ANNAMARIE

GIGI

JOSIE

SAL

LILY DINO GINO

CARMELA

GIAN

GIANCARLO

UCHE

GLADIS LEONOR

RICO ARIANNE

NINO ROSANNA

LORENZO

RUBY

CARMELITA

LANI

LIZA JAYSON

LOUIE MANNY

ELY

YANET

ZORAIDA

NELLY

LUZ OLGA

VIOLETA

GIA

LEILANI

TESS

LITA

NOEL

MARLYN

REY

CARMELO

DANIA BELKIS

MAGALY

BRENNA

LINDSAY MEGHAN

JACLYN

RACHEL

JANELLE

NICOLE

JESSICA

DANIELLE

JILLIAN

MEAGHAN

SARAH

KIRSTEN

SAMANTHA

NATALIE

ERICA

MICHEAL

SYLVESTER

JACI

RENAE TRACI

DANELLE KELLI

JENI

STACIA

TAMRA

TRACIE

JAMI

MELODIE

DEANA

LYNDSEY

HILLARY

DEANN

JAYME

MANDI

STACIE

KRISSY KRISTY

LINDSEY

KATELYN

JEANA

JOHNNA

STACI

KACIE

SADIE BROOKE MALLORY

KAITLIN

CAITLIN BRIDGET

JULIANNE

JENNI

KRISTINE

RACHELLE

KRISTINA

NOELLE

CHRISTINA

GABRIELLE

KANDI

HOLLIE

JEANNA

CORI

CHRISTIE

BETHANY

REBEKAH

DARCI

CRISTIN

TANA

CHRISTEN

LEXIE

LAYNE

MINDI

JANELL SHELBY

TRISTA

CASSIE

MACKENZIE

KIRSTIN JENNA

CARLY

LYNDSAY

RACHAEL

MANDY

NICKI JODIE

SHAYNE

KATE KAT

LISSA JULIANNA

GENEVA INEZ

KELSIE

JESSI

CARLEY

PAIGE

KAITLYN

MADDIE

CAITLYN

QUINTON

AJA

CIERRA

MYLES

CONRAD

KENDRICK

JARVIS

TERA

MISTI

CHRISTI

KAYLEE

BECCA

BREANNE

JAIMIE

HANNAH SKYE

STEPH

ROBYN

HILARY

ELLIE

MADDY SERENA

YVETTE

ERICKA

KASEY

EMILEE HALEY

KYLEE

TORI

KELSEY

MEAGAN

KRISTYN

WENDI

ANDIE

RHIANNON

TESSA

KERRIE

CATE CARIN

SUZAN

KAYLEIGH

LEANNE

LEEANNE

KATHARINE

CATHRYN SUZANNA

SUSANNAH PENELOPE

KELI

JESSIKA

MINNIE

CORRIE

TRACEY

KYLIE

HAYLEY

CHLOE

ALISON

ADELE

PHOEBE

MARLENA JANEEN NIKI NINA MIA LOLITA

JOCELYN

SHIELA

ROMEO

ROMMEL

DINA DALIA

INDIRA

XIOMARA

CIARA

JADE

KIMBERLEE STEFANI

LIZZY

GRETA

ANNABEL IDA

COLLETTE

SOPHIA

SELINA DAVINA

RACHEAL

JANA

ALEXIS NONA

THEA

JASMIN

RICHELLE

LISBETH

JACKELINE

MARLEN

ELENA

SOFIA

FABIOLA

MOISS

ELIEZER

PALOMA

LETICIA RAQUEL ESTER

ELISA MARIA

CLAUDIA

INS

LILIANE

ROSILENE MARLIA RICA

ANY

ANGLICA

ANA

ANTONIA

ISABEL CELINA

MARCELA

SILVIA

MARTA

VALERIA

NATY

SU

NURUL NOR

MB

FER

KARINA

LUCIA

GABRIELA

MARIANA

MARISA

MONI CARINA

RAISSA

LVIA NGELA

ANGELITA

EDNA

CELIA

SONIA MARI

ELIDA LIDIA

CRISTINA

DANIELA

NATALIA

VALE

EMA

CLARA

FRIDA

REBECA

SOCORRO

CABAAS

WANG YU

NG

LILI

LUIZA

JOANA

GISELLE

NATALI

IVANA

CLAU

IZABELA TNIA

WEI

TATIANA

MARINA

REGIS

AURA

AMPARO

CINTHYA

EVELIN

DALILA

ELOISA

IKE RHEA

MARILOU

FE FERDINAND

REYNALDO TITO

ENA ASTRID

JOHANNA

HEIDY

KATTY NIDIA

ELISHA

DINAH ALEXA

ALEXANDRA

LEA

KIMBERLEY

TANYA

NATASHA

SASHA

ALBERTA

ETHEL

VIOLA

NAOMI

DANICA

CORA

TAMARA

CHANTELLE

NICOLETTE

BETTIE

BESSIE

ERMA CECELIA

SONJA

ALEX ALEXANDER

ELLA

LJ CHER

RHODA

SAUNDRA

BETTYE

FREDA

BLANCHE BERNICE EDWINA

HAZEL

LORNA

DAPHNE

NITA

RONA

ROWENA

JUN

ARNEL

MARICEL ERWIN VIRGILIO

INGRID

SINDY

YULIANA

LINA

LUISA

ALBA KENIA

SORAYA

ALDO

ALE

JOHANA JANETH

ROMY

WILLY

EDDY ELVIN

WILFREDO

WILMER

RONY

FREDY

LEIDY YENNY

MERY

KAYODE

JOSEFA FRANCISCA

TANIA

ISA

VILMA

EMILIA

ADRIANA

MARTINS

MILENA

JOSIANE

ARIANE

FELIPE LILIAN

MAGDA

LIGIA

SILVANA

LAS

GISELE

JANANA

KAMILA

RODRIGO

MIRIAN CINTHIA

MAIRA

ELIANA

ROSANA

LUDMILA

RAFAELLA

ISADORA

AUREA

ANNY AUGUSTO

HELLEN

LEONARDO

CLAUDIO

SAMANTA

DAIANA

FLAVIA BRBARA

DBORA

LUCIO

MAURO

SILVIO FABRICIO

MARCELO

ROSI

CINTIA

CAMILA

NATHALIA

NATLIA

CATARINA

RUI HELENA

LI

LUCIANA FERNANDA

MARCOS

FLAVIO

DANILO

LUCIANO LEANDRO LUCAS FABIANA

RAPHAEL

MARGARETH

FLVIA

JULIANA

LISANDRA

REINALDO

DEISY

MAMIE

ELOISE

ROCHELLE

BONITA

LAVERNE VERNA

PEARL

DARA ROZ

RENA

EDEN

MAE

EVANGELINE

KAYLA HAILEY

LEXI

MIRANDA

BRIANNE

DAYNA

KIRA

LEISA

BRANDIE

HALLIE JENA

OTIS

CLARENCE

CANDI

SHEA

MALIA

PATIENCE

JORDYN

COURTNEY NIKKI

TARYN

SYDNEY

LACY

MACY MISTY

CASSIDY CALLIE

CORTNEY

CHELSEY

KARISSA

NICHOLE

ELAINA

KYRA

ASHLEIGH

KYM

SHANNA

BROOK

LACEY

KACEY

KENDALL

KYLA

SHAYNA

HOPE FAITH JOY

KENDRA

JENIFER

SHAINA

KIMBERLY

JACQUELYN

SHEREE

LYNETTE

JOLIE

JENELLE FARRAH

SHANA

CANDACE

CANDICE KATRINA

ROBBIN

WILFRED

DELORES

DELORIS

HENRIETTA

SYBIL

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TABLE II: The five nearest neighbors(NN) of representative male and female names in embedding space, showing how they preserve associations among Asian (Chinese, Korean, Japanese, Vietnamese), British, European (Spanish, Italian), Middle Eastern (Arabic, Hebrew), North American (African-American, Native American, Contemporary), and Corporate/Entity.

REPRESENTATIVE

SENATOR

MIKEY

ELVA

LETTY

REM ANAIS

ELI

G

is a variant of “Daren” and “Darien”, among others. Fig. 3 (left) presents a map of the top 5000 last-names. We color a name according to the dominant racial classification from the Census data. The top 5000 names contain four races: White (pink), African-American (orange), Hispanic (yellow), and Asian (green). Names without a dominant race are colored gray. The three cutouts in Fig. 3 highlight the homogeneity of regions by cultural group. The embedding clearly places White, Hispanic and Asian in large contiguous regions. African-American names are more dispersed. Interestingly, there are two distinct Asian regions in the map. Fig. 4 presents insets for these two regions, revealing that one cluster consists of Chinese names and the other Indian names. Overall, Fig. 2 and Fig. 3 show that our name embeddings capture gender and ethnicity information well.

is the frequency of names in the Contact list. The replacement usage frequency is the frequency of a name in the replacement name population. To measure the popularity dis-similarity, we sample 10k names randomly from the name list, with sampling probability proportional to real name frequency. We record ten nearest neighbors (NN) for each of them. Now the popularity dis-similarity is computed by JensenShannon Divergence using the real name frequency of the sampled names and replacement usage frequency of the nearest neighbor names. Secondly, we measure gender similarity by precision at k, defined as the percentage of the k-nearest neighbors of a name having the same gender as the name itself. Finally, we measure ethnicity similarity by precision at 1. For example, the precision for White names is defined as P (W |W ) = P (1st NN is White|real name is White). Results. We present the evaluation results in Table IV. The table shows that a joint embedding of first and last names are often better than the separate embedding. In addition, the CBOW model generally outperformed the SG model for the majority of the nearest neighbor tests. Given these observations, in this paper by default we use “CBOW joint”.Note that while P (B|B) (35%-59%) is generally much lower than P (W |W ) (92%-94%), considering that a randomly picked name from the contact list has a probability of 74% of being White but only a probability of 3% of being Black, P (B|B) is actually significantly above the probability of a random name being black.

D. Evaluation of Different Word2vec Embeddings The embedding from word2vec is influenced by two factors: the input text, and the word2vec parameter settings. So far we have been using the contact lists unchanged as the input to word2vec. However there are many possible variants. Since certain contact lists are short, and word2vec uses a sliding window, some names from the contact lists of the previous/next persons could be in the same window as that for the current person. One possible solution is to pad stop words in between two contact lists such that a sliding window would never enclose contacts from unrelated persons. Instead of putting both first and last names together in one embedding space, another variant might generate two embeddings, one using only the first names, and another using the last names. In addition to the input text, the second factor that influences the embedding is the different settings of models and parameters, for example, the selection between CBOW model and SG model, the size of sampling window and the size of negative samples. To understand how these two factors influence the embedding, in particular with regard to the quality of the resulting look-alike names, we evaluate the following variants of the word2vec embeddings: • Set the word2vec model to be CBOW or SG. • Generating joint embeddings of first names and last names using the contact lists as they are (“CBOW joint” or “SG joint”). • Generating embedding for first names and last names separately by including only first/last names in the contact lists (“CBOW sep” or “SG sep”). Metrics. To evaluate the quality of the embeddings with regard to look-alike names, we propose three metrics to measure popularity, gender and ethnicity similarities between real and look-alike names. Firstly, we would like the overall frequency of a name in the real contact lists to be similar to that in the look-alike lists. For example, if “Mark” is a more popular name than “Barnabas” in the real contact lists, we would also expect that “Mark” appears more often as a replacement name than “Barnabas”. We define two types of frequencies. The real name frequency

III. P ROPERTIES OF NAME E MBEDDINGS After all the names in the contact lists have been embedded through word2vec, each name is represented by a vector in the high dimensional space. Earlier, in Fig. 2 and Fig. 3, we have provided visual evidence that the embedding is coherent, in the sense that it places names of similar gender and ethnicity close-by. In Section II-D we have also seen aggregate numerical evidence of this coherence. In this section we evaluate the coherence of the name embedding quantitatively and in detail. A. Gender Coherence and Analysis We first examine the gender coherence of a subset of first names and their ten nearest neighbors. This subset of first names is the intersection between contact lists and Census 1990. It contains 1,146 unique male first names and 4,009 unique female first names. All names in the subset are ranked by their popularity as measured in the Census 1990 data. Table V shows the gender coherence results, measured by precision at k, as a function of the population of the names, and k, the number of nearest neighbors. For example, the cell at {≤ 20%, 2} of Table V (left) reads 97. It means that for the top 20% most popular names, 97% of their nearest 2-neighbors have the same gender as them. To save space, we only report the first two significant digits of each precision (e.g., 0.9742 becomes 97). In addition we color the cells of the tables based on the values. Within each table, we use warm colors for high values and cold color for low values. This gives us heat-maps through which it is easier to see the trend of how the precision

4

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Fig. 3: Visualization of the name embedding for the top 5000 last names, showings a 2D projection view of the embedding (left). Insets (left to right) highlight British 1 , African-American 2 and Hispanic 3 names. Variation CBOW joint SG joint CBOW sep SG sep

Popularity 0.6434(0.0007) 0.6747(0.0002) 0.6675(0.0003) 0.5776(0.0001)

Gender NN(1) NN(10) 0.9092 0.9360 0.8844 0.9274 0.9162 0.9350 0.8844 0.9205

P (W |W ) 0.9362 0.9461 0.9299 0.9217

Ethnicity (NN(1)) P (B|B) P (A|A) 0.5939 0.7626 0.4561 0.7208 0.4437 0.7167 0.3451 0.6797

P (H|H) 0.7543 0.7543 0.6710 0.6971

TABLE IV: Evaluation of different embedding variants. Popularity is measured by Jensen-Shannon Divergence of frequency distribution, while all other values are precision at k. Top % 1 2 3 4 5 6 7 8 9 10 ≤ 10% 100 99 98 98 98 98 98 98 98 98 ≤ 20% 99 97 96 96 95 95 95 95 95 95 ≤ 30% 96 95 94 93 93 92 93 92 92 91 ≤ 40% 93 93 91 90 90 89 89 89 88 88 ≤ 50% 89 89 86 85 85 84 84 84 83 83 ≤ 60% 86 86 84 83 82 82 82 81 81 80 ≤ 70% 82 81 79 79 78 77 77 76 76 76 ≤ 80% 79 78 76 75 74 74 74 73 73 72 ≤ 90% 76 75 73 73 72 71 71 71 70 70 All 73 72 70 69 69 68 68 67 67 67

varies with popularity of the first name, and the number of neighbors. From Table V, we observe that our proposed name embedding scheme shows strong gender coherence, especially for popular names. As we can see from the tables, the percentage of neighbors that have same gender as the original first name is very high for the top 30% most popular names comparing to a randomly assigned name (50%). On the other hand, the percentage decreases when unpopular names are included, and also decreasing as the number of neighbors increases. A similar coherence analysis was carried out with regard to ethnicity. We found that the top neighbors of a popular

Top % 1 2 3 4 5 6 7 8 9 10 ≤ 10% 97 97 97 96 95 95 95 95 95 95 ≤ 20% 91 91 91 90 89 89 89 89 88 88 ≤ 30% 85 84 84 84 83 83 82 82 82 81 ≤ 40% 80 79 79 78 78 77 77 77 76 76 ≤ 50% 75 74 74 73 73 72 72 72 71 71 ≤ 60% 69 69 68 68 67 67 66 66 66 66 ≤ 70% 66 65 66 65 64 64 63 63 63 63 ≤ 80% 62 61 61 61 60 60 59 59 59 59 ≤ 90% 59 59 59 58 58 57 57 57 57 57 All 57 56 56 56 55 55 55 54 54 54

TABLE V: Gender coherence of the name embedding for males (left) and females (right), as measure by the percentage of k-neighbors being male (left) and female (right). name tend to have a high probability of being in the same ethnicity group. The coherence for an ethnic group correlates positively with the popularity of the group in the contact lists. The coherence for AIAN and 2PRACE are poor, because they only account for 0.1% and 0.05% of the last names in the contact lists. Thus there is too little data to get the embedding correct.

First names Last names

PCC RA RU 0.5813 0.7795 0.2260 0.4454

SCC RA RU 0.5170 0.5402 0.3444 0.3916

TABLE VI: Correlation of real names and replacement names. B. Name Popularity Analysis To measure the popularity preserving of word embedding, we calculate the real name frequency of a name (R), the average real name frequency of its replacement names (ten nearest

Fig. 4: The two distinct Asian clusters. Left: Chinese/South Asian names ( 4 in Fig. 3). Right: Indian names ( 5 Fig. 3).

5

Gender distribution. R-Random,G-Generative

0.35

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neighbors) (A), and its replacement usage frequency (U ). Two measurements, Pearson’s correlation coefficient (PCC) and Spearman’s rank correlation coefficient (SCC), are used to measure how well the popularity is preserved. The results are shown in Table VI. Overall we can see that the correlation between the real name frequency R and the replacement usage frequency U is higher than that for the real name frequency R and its neighbors’ real name frequency A. This indicates that a popular name is very likely to appear in among the nearest neighbors of other names, even though its nearest neighbors are not necessarily popular names.

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Fig. 5: Left: the distribution of user’s gender in contact lists data. Distributions with legend “R” are from binomial distribution with probability 0.5. Distributions with legend “G” are from binomial mixture model with parameters inferred using EM algorithm. Other distributions are from observation in the contact lists. Right: deviation from the null hypothesis.

C. Why does the embedding work? Recall that we start from contact lists of millions of users, and embedding the first and last names using word2vec. We have observed visually and though numerical metrics that the embedding captures gender and ethnicity very well. A natural question is: why does it work so well? Our hypothesis is that the population as a whole have a bias toward contacting people of the same ethnicity and gender. This bias drives the embeddings of names of similar ethnicity and gender close to each other, and pushes names of different ethnicity and gender apart. To test this hypothesis, we look at the frequency distribution of percentage of males in our mailing list, and compare with the null hypothesis. In Fig. 5 (left), we divide contact lists by a threshold based on the minimum number T of identifiable genders in the list. E.g., T = 5 means those contact lists with at least five genderidentifiable names. The distributions of the ratio of identifiable males in the contact lists with T = 5 and 10 are seen as the two lower curves. Clearly, the majority of the contact list has around 50% males. However, looking at these distribution along would not tell us whether the distributions have any bias. For this purpose, we need to compare them with the null hypothesis. We generate the null distribution by assigning the gender of a name randomly following the gender distribution of the contact list. Fig. 5 (left) shows that the distributions based on the null hypothesis (the two higher curves) are spikier, with around 30% of the contact lists having 50% of males, compared with the observed 15%. Fig. 5 (right) shows the deviation of the observed distribution from the null hypothesis. It shows a clear bimodal pattern, confirming our hypothesis that contact lists on average exhibit a bias towards either male domination, or female domination, especially the latter. A similar analysis was done with regard to ethnicity, and confirmed a bias for people to contact other people of the same ethnicity. Due to space limition details are ommitted. To further verify the gender bias in observed contact lists, we model the observed number of males in all contact lists as a Binomial mixture model. The mixture model fits the observed data well, and suggests that 47% of the observed contact lists belong to male users and 53% belongs to female users.

IV. C ONCLUSION We propose a new technique for generating look-alike names through distributed name embeddings. By training on millions of email contact lists, our embeddings establish gender and cultural locality among names. The embeddings make possible construction of replacement aliases for any given name that preserve gender and cultural identity. Through large-scale analysis of contact lists, we establish that there is a greater than expected concentration of names of the same gender and race for all major groupings under study. The lookalike name generation technique is currently being deployed as part of soft-challenge pipeline for login challenge and account recovery see Fig. 1). A demostration website for generation look-alike names is available as http://yo/lookalikename. We are current using these embeddings for age and ethnicity classification of names. Gender results are available in the URS datahub and ethnicity results will be in soon. R EFERENCES [1] T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems, 2013, pp. 3111– 3119. [2] C. Bureau, https://www.census.gov/topics/population/genealogy/data/ 1990 census/1990 census namefiles.html, 1990. [3] ——, https://www.census.gov/topics/population/genealogy/data/2000 surnames.html, 2000. [4] R. Gross and A. Acquisti, “Information revelation and privacy in online social networks,” in Proceedings of the 2005 ACM workshop on Privacy in the electronic society. ACM, 2005, pp. 71–80. [5] open source project, https://code.google.com/archive/p/word2vec/, 2013. [6] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” in Proceedings of Workshop at ICLR, 2013. [7] L. Van Der Maaten, “Accelerating t-sne using tree-based algorithms,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 3221–3245, 2014. [8] Y. Hu, E. Gansner, and S. Kobourov, “Visualizing graphs and clusters as maps,” IEEE Computer Graphics and Applications, vol. 30, pp. 54–66, 2010. [9] M. Campbell, http://www.behindthename.com, 1996.

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