By number person search security social

Prior to , cards were issued in local Social Security offices around the country, and the area number represented the state in which the card was issued. Since , when the SSA began assigning numbers and issuing cards centrally from Baltimore, the area number is assigned based on the zip code in the mailing address provided on the application for the original Social Security card.

A word of warning: the applicant's mailing address may not be the same as his or her place of residence. Therefore, the area number does not necessarily represent the applicant's state of residence either prior to , or since.

Social Security Death Index

The area numbering scheme was developed in , before computers, to make it easier for the SSA to store the applications in Baltimore files that were organized by regions and alphabetically. Originally, it was intended for SSA internal use and convenience, and was not intended for anything more. However, it's a good clue for the family sleuth! Since , the SSA has used an electronic system, or computer, to maintain records of approximately 60 million deaths that have been reported to them.

This database is in tape format, which is not searchable by the public. However, the U. Department of Commerce does sell these reels of magnetic tape to genealogical services that reformat the information on their own searchable computer databases or publish it on cd-roms. These include Social Security number, last name and first name, date of death and date of birth, zip code of last residence, and zip code of lump sum payment recipient.

As with any electronic data, problems exist in the original database, and these errors flow through to all versions of the Social Security Death Index. For example, the SSA database allows only twelve letters for last name and nine letters for first name, with all other letters being truncated, or left off.

Also, data entry errors do occur. If you can't find someone by first and last name and birth date, try searching by first name only and as much other information as you can to narrow the search. Be sure to visit Kathleen Hinckley's Family Detective web site. In addition to data entry errors, be aware that the death date may contain month and year only, especially before Another issue is that the zip code information may lead you in the wrong direction. Zip codes were not used until , and the location assigned to a zip code is based on U.


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Postal Service assignment of localities to a given zip code. This may not be the town where the person actually lived, nor where final benefits were sent. For example, a zip code of results in two Missouri town names-Chesterfield, and Town and Country. Do not be fooled into thinking the zip code or locality of last residence is where the person died. They may have last resided in Patterson, Missouri, but actually died in a hospital in Memphis, Tennessee.

In that case, you would never find a death certificate in Missouri. Having told you all the pitfalls to watch out for, I will say that you can still find many valuable clues in the SSDI. Let's start with who is not in the SSDI. Everyone who received a Social Security number or paid withholding tax is not in the database. Based on such limited knowledge, SSN inferences described in the literature would start from known SSNs and predict, based on their digits, the possible states and ranges of years when those SSNs could have been issued We conjectured, however, that the functional relationship between the digits of an SSN and the location and time of its application could be reversed, allowing the inference of all of the 9 digits of unknown SSNs starting from their presumptive state and day of application.

Empirical observation of SSA's policies—particularly the Enumeration at Birth EAB initiative, which started extending nationwide in 2 —drove the conjecture the EAB was designed as an antifraud program integrating the application for an SSN into the birth certification process. After EAB, the overwhelming majority of U. Although the assignment process remained inherently noisy, we hypothesized that i times and locations of individuals' SSN applications over time have become more correlated with those individuals' times and states of birth; ii such correlation may allow a more granular understanding of the SSN assignment scheme and its regularities than what is currently described in the literature; iii this more granular understanding, coupled with the increasing correlation between births and SSN applications, may allow the prediction of unknown SSNs entirely from the applicants' birth information.

Ironically, one of its applications is fraud prevention, because the DMF can be used to expose impostors who assume deceased individuals' SSNs. The process of discovery of a more granular understanding of the SSN assignment patterns was iterative: We used public information about the assignment scheme to analyze publicly available data; this allowed us to reinterpret public details about the assignment scheme and analyze the data again under improved lenses. We split DMF records into groups by their state of application, and—within each group—sorted them chronologically by birthday.

If our hypothesis was correct, we would observe individuals with close birthdays and same state of application display similar SSNs in the rearranged dataset. Thereafter, we would be able to use such regularities to predict unknown SSNs based on birth information.

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After grouping and sorting DMF data by state of assignment and date of birth, we started looking for visual and statistical patterns in the rearranged dataset that proved or disproved the connection between birthdates and SSNs. The analysis confirmed the regularities we expected: As hypothesized, a strong correlation exists between dates of birth and all 9 SSN digits; that correlation increases for individuals born in years after the onset of the EAB program, and in less populous states where fewer births take place over a given period, determining slower—and more detectable—transitions through the SSN assignment scheme.

In Fig. SSNs of DMF records sorted by state of assignment and ordered by date of birth for 2 representative states in and The x axis represents time: the day of birth, over days in or , for individuals whose deaths were reported to the SSA and whose SSNs were assigned in Oregon or Pennsylvania. Specifically, GNs transition slowly or remain constant over the years selected for Fig.

ANs transition faster than GNs; however, contrary to a commonly held view about their assignment, the same AN is used for 9, consecutively assigned SSNs. Such scheme would render the AN random for states with multiple ANs, and the predictions we present in this article dramatically less accurate.

Instead, Fig. The speed at which they change, coupled with the noise and idiosyncracies inherent in their assignment, may suggest that the relationship between dates of birth and SNs is, for practical purposes, random. However, visual observation of the SN subplots in Fig. The steepness of the imaginary line interpolating the SNs is a function of the state's volume of births over a period: At least 5 upward sloping and approximately parallel trend lines emerge in the SN portion of Fig.

Based on visual inspection [and statistical analysis presented in supporting information SI Appendix ], we gained a different and more granular understanding of the regularities in the SSN assignment pattern than what is currently discussed in the literature. We concluded that the combined SSN assignment scheme consists of SNs transitioning first; after 9, SNs associated with a certain combination of AN and GN, the next AN in the issuance scheme is assigned; then, when all ANs assigned to a state or territory are exhausted, the next GN in the scheme is assigned.

More importantly, we concluded that the linearity in the assignment of SSNs can be publicly observed as a pattern linking applicants' dates of birth to their SSN digits, including their last 4. The assignment patterns that Fig. Our prediction algorithm exploits the observation that individuals with close birthdates and identical state of SSN assignment are likely to share similar SSNs.

We predict a target individual's first 5 SSN digits that is, his or her ANGN by choosing the statistical mode of the distribution of ANGN s appearing in the set of DMF records whose birthdates are contained within a variable window of days centered around that target individual, excluding the target record from the set. Because the 50 states greatly differ in numbers of births occurring over a given period, they exhibit different transition speeds across the assignment scheme.

As described in SI Appendix , we calculated such variable windows of days to account for such differences.

Best Way To Find People By Social Security Number

Furthermore, various outliers can be found among DMF records data entry errors or individuals—such as aliens—who received SSNs later than at birth. We describe data-cleansing procedures in SI Appendix , although our prediction accuracy tests also included outliers. We predict a target individual's last 4 SSN digits that is, his or her SN using the set of SSNs of all DMF records contained in the variable window of days centered around the target individual's birthdate, and regressing the SNs of those records on their associated birthdates excluding the target record from the set.

The regression model is sketched in Eq. For the tests presented below, we used robust regressions. Variations of the algorithm are discussed in SI Appendix. We evaluated the performance of our prediction algorithm using the DMF as an analysis set to identify assignment patterns, and as a test set to measure the accuracy of SSN predictions based on extrapolated patterns.


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  4. Naturally, the analysis set used in the prediction of a given DMF record did not include said record. As hypothesized, although our predictions are already more accurate than random chance by several orders of magnitudes over the through period, dramatic and widespread increases in accuracy are especially observable after the onset of the nationwide EAB program , particularly for less-populous states.

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    Furthermore, a trend of steady improvements in accuracy is evident over the years across all states, as increasingly larger proportions of newborns receive their SSNs through the EAB program data scarcity does not determine this result, as discussed in SI Appendix. Prediction accuracies for DMF records with January to December birthdays across the 50 states.

    In each quadrant, columns represent months, and rows represent states sorted by their births, lowest to highest.

    The Purpose of Having a Social Security Number

    The colors in each cell represent ratios out of monthly SSN counts. For the last 4 digits, we considered a brute-force matching algorithm where, for each target SSN, the attacker tries out the predicted ANGN and SN combination, before increasing and decreasing the SN by 1-integer steps for the subsequent attempts, while keeping the predicted ANGN constant. Nationwide, the weighted mean of the percentage of whole SSNs that can be matched with or fewer attempts is 0.

    In practical applications, SSNs are often used as authenticators in inquiries processed by credit reporting agencies CRAs.