## 4 Constructing Realistic Fake Identities
### Derived Fields
* **First Name**: separate first name lists based on U.S. Census data were used for each of the male and female IDs. A larger list of female names was used than male names given what appears to be higher variability (particularly in spellings) in female names. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PRNG Output** & **ID Characteristic** & **Distribution Shaping** & **Rationale** \\ \hline
0 & Sex and & Male: 50\%, Female 50\% & 2010 Census [108] \\  & Preferred Pronoun & Pronouns assigned to sex & \\ \hline
1–2 & First Name & Weighted 15 k (male) and 20 k (female) & Social Security \\  & & most common names & Administration [109] \\ \hline
3–4 & Last Name & Weighted 20 k most common surnames & 2010 Census [110] \\ \hline
5–7 & Birthdate & Adult Ages (18–74), 5-Year Bracketed & Census [111] \\  & & Frequencies by Sex & \\ \hline
8–9 & Email Password & Unique per ID & \\ \hline
10 & Email Username & Unique per ID & \\ \hline
11–17 & Address & Unweighted, Pseudo-random Street & 2020 Census [112] \\  & (split into 5 columns) & Assignment based on Geographical Frequency & Open Address [113] \\  & & by State, Invalidated by the USPS API & USPS [114] \\ \hline
18 & Security Response 1 & Uniformly Assigned List of Popular Movies & \\ \hline
19 & Security Response 2 & Uniformly Assigned List of Cities & \\ \hline
20 & Security Response 3 & Uniformly Assigned List of Colors & \\ \hline
21 & Political Affiliation & Democratic 51\%, Republican 47\%, & 2020 election [115] \\  & & Third-Party 2\% & \\ \hline
22–24 & Income & $15 k–825 k : 10\%, 525 k-835 k : 11\%, & Statistica [116] \\  & & & $35 k–850 k : 14\%, 550 k-875 k : 20\%, & \\  & & & $575 k–8100 k : 15\%, 5100 k-8150 k : 20\%, & \\  & & & $150 k–8200 k : 10\% & \\ \hline
25 & Education Level & Weighted Educational Attainment Brackets & Census Bureau [117] \\ \hline
26 & Education Major & Weighted Educational Major Brackets & Statistica [118] \\ \hline
27 & Sexuality & Sexual Orientation and & Gallup [119] \\  & & Identity by Generation & \\ \hline
28 & Salutation & Associated by Sex & \\ \hline
29–33 & Reserved & & \\ \hline
34–35 & Height & Gaussian with U.S. mean \(\pm\) 2\(\sigma\) & CDC 2015-2018 [120] \\ \hline
36–37 & Weight & Gaussian with U.S. mean \(\pm\) 2\(\sigma\) & CDC 2015-2018 [120] \\ \hline
38 & Job Title & Weighted National Employment Matrix & U.S. Bureau of \\  & & Labor Statistics 2021 [121] \\ \hline
39 & Race & White 75.80\%, Black 13.60\%, Asian 6.10\%, & U.S. Census Bureau \\  & & Other 4.50\% & Population Estimates [122] \\ \hline
40 & Nationality White & Hispanic 18.90\%, Non-Hispanic 81.10\% & U.S. Census Bureau \\  & & Population Estimates [122] \\ \hline
40 & Nationality Black & Hispanic 2\%, Non-Hispanic 98\% & Pew Research [123] \\ \hline
40 & Nationality Asian & Hispanic 0.002\%, Non-Hispanic 99.998\% & Pew Research [124] \\ \hline
41 & Website & Assigned based on Research Question & \\ \hline
42–255 & Reserved & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mapping of PRNG word outputs to foundational fake ID characteristics. * **Sex**: these values were assigned 50/50 instead of 49.2 and 50.8; for any experiments where those distinctions are relevant, we anticipate more extreme sculpting of gender identities. * **Birthday/Age**: the PRNG-based mapping creates a birthdate, while age is directly calculated with the current date at any usage of age. * **Address/State**: state was selected first and an address was generated for that state. * **Address**: uses the USPS API (usps-api 0.5) [114] to determine if a generated address is fake or not. Returns true or false, with all street numbers modified until the result is confirmed as false. * **Height/Weight**: once Height has been selected from a percentile-based distribution [120], a conditional distribution was used to select a corresponding weight that is within two standard deviations of the height-adjusted median.