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Mate1.com was hacked on October 17th, 2015 and a copy of profile data for 27,403,958 accounts has been obtained by LeakedSource.
Preface Mate1.com was hacked on October 17th, 2015 and a copy of profile data for 27,403,958 accounts has been obtained by LeakedSource. You can search for yourself in the hacked Mate1.com […]

HEROIC Cybersecurity

July 29, 2024

Preface

Mate1.com was hacked on October 17th, 2015 and a copy of profile data for 27,403,958 accounts has been obtained by LeakedSource. You can search for yourself in the hacked Mate1.com database and many others on our main site. We found out about the hack via vice and spent over a month looking for the data as well we ran some interesting statistics on the data and here they are presented concisely. If you are in this database, contact us and we will remove you from our copy for free.

Anyone may use the information on this page for free in any capacity provided LeakedSource is given credit and a link back.

Table of Contents

Passwords

Passwords were stored in plain, visible text meaning there was no encryption whatsoever and all users who registered now have their credentials plainly visible. This is not what internet standards propose.

The following table is the top 30 passwords used, we have grouped them case insensitive such that using ‘Password’ will count for ‘password’ for illustrative purposes.

 

Rank Password Frequency
1 123456 380,554
2 123456789 181,268
3 123 167,773
4 12345 106,219
5 1234 55,579
6 password 44,572
7 1234567 43,315
8 love 40,618
9 12345678 37,383
10 iloveyou 32,466
11 1234567890 30,484
12 111111 25,776
13 qwerty 24,315
14 mylove 16,598
15 sex 16,231
16 loveme 16,211
17 123123 15,894
18 000000 15,356
19 sexy 15,271
20 mate1 14,143
21 pussy 14,075
22 nothing 13,931
23 football 13,703
24 hello 13,208
25 654321 12,828
26 abc123 12,821
27 mother 12,684
28 computer 12,427
29 lovely 12,416
30 london 11,888

Countries

Country data is stored using internal numeric identifiers that we used best guess based on postal code and email address to reverse. India and China seem hopeless.

 

Rank Country Frequency % of Users % Male % Female
1 United States of America 17,844,060 65.11 69.88 30.12
2 United Kingdom 2,379,663 8.68 80.19 19.81
3 Canada 1,639,967 5.98 75.06 24.94
4 Australia 846,873 3.09 74.45 25.55
5 India 512,964 1.87 96.06 3.94
6 Unknown(164) 330,508 1.21 82.33 17.67
7 China 318,016 1.16 94.47 5.53
8 Ireland 216,135 0.79 78.08 21.92

Zip Codes

Postal codes are also stored in this database and allows us to determine where people are registering from. Many of them appear to be falsely entered, but we are not here to interpret the data, only present it. Here they are in entirety.

The following table is the top 30 zip codes.

 

Rank Postal Code Real Location Frequency
1 10010 New York, NY 197,227
2 10001 New York, NY 193,640
3 20109 Manassas, VA 71,608
4 90001 Los Angeles, CA 64,643
5 85281 Tempe, AZ 52,631
6 84332 Providence, UT 50,127
7 50001 Ackworth, IA 28,801
8 75002 Allen, TX 25,489
9 19801 Wilmington, DE 23,348
10 60601 Chicago, IL 22,675
11 10118 New York, NY 21,961
12 12345 Schenectady, NY 20,778
13 90210 Beverly Hills, CA 18,011
14 10011 New York, NY 17,909
15 00233 Koforidua, Ghana 17,841
16 10009 New York, NY 16,545
17 10002 New York, NY 16,247
18 n/a N/A 14,403
19 CF991NA Cardiff, United Kingdom 14,284
20 g23dh Sauchiehall St, Glassglow, Scotland 13,622
21 10012 New York, NY 13,410
22 60611 Chicago, IL 12,936
23 90503 Torrance, CA 12,433
24 60606 Chicago, IL 12,413
25 30303 Atlanta, GA 12,122
26 92101 San Diego, CA 11,961
27 10021 New York, NY 11,428
28 95133 San Jose, CA 10,985
29 20001 Washington, DC 10,701
30 73301 Austin, TX 10,642

 

Genders

Gender ratios on dating sites are always of interest. Here is how Mate1.com user base is split. 6 Accounts have an unknown gender, these are probably staff test users. The ratios are about industry average according to other leaked databases.

 

Rank Gender Frequency % of users
1 Male 19,733,020 72.01
2 Female 7,670,932 27.99

Email Addresses

Top 50 overall domains. Whether real users or not, 15,103 emails end in *.mil and 2,311 end in *.gov

 

Rank Email domain Frequency
1 @yahoo.com 10,686,469
2 @hotmail.com 4,218,301
3 @gmail.com 4,160,689
4 @aol.com 1,432,337
5 @hotmail.co.uk 511,324
6 @yahoo.co.uk 343,544
7 @live.com 325,425
8 @msn.com 265,070
9 @ymail.com 262,778
10 @comcast.net 212,673
11 @yahoo.ca 128,312
12 @sbcglobal.net 121,847
13 @outlook.com 112,224
14 @yahoo.co.in 111,694
15 @live.co.uk 104,287
16 @rocketmail.com 98,905
17 @att.net 87,012
18 @mail.com 79,293
19 @verizon.net 78,618
20 @yahoo.fr 77,356
21 @bellsouth.net 61,415
22 @aim.com 61,285
23 @rediffmail.com 58,300
24 @btinternet.com 57,398
25 @yahoo.com.au 57,076
26 @cox.net 53,572
27 @icloud.com 51,134
28 @live.ca 49,518
29 @googlemail.com 48,573
30 @earthlink.net 46,287
31 @excite.com 42,632
32 @juno.com 36,897
33 @me.com 36,547
34 @netzero.com 33,345
35 @bigpond.com 33,326
36 @hotmail.fr 32,520
37 @charter.net 31,999
38 @hotmail.ca 31,234
39 @shaw.ca 30,208
40 @mail.ru 29,660
41 @live.com.au 28,224
42 @netzero.net 26,339
43 @email.com 25,619
44 @netscape.net 24,092
45 @breakthru.com 23,606
46 @peoplepc.com 22,958
47 @rogers.com 21,689
48 @sky.com 20,402
49 @yahoomail.com 20,357
50 @ntlworld.com 20,017

Site Growth

We can determine Mate1.com site growth by looking at how many users register each year. 3,170 accounts had invalid join dates.

Interestingly, 2,987,565 of all accounts ever created have logged in on or after January 1st, 2015 however 2,521,390 accounts were created in 2015 up until hack point which suggests that since Mate1.com’s inception only 466,175 users from 2014 or earlier have used the site recently.

Furthermore, 25.17% of all users did not return to Mate1.com after their initial registration (join date = last visit date).

 

Year Registered New Users % Male % Female
2002 85,083 56.63 43.37
2003 261,569 57.01 42.99
2004 370,660 88.28 11.72
2005 2,521,550 81.82 18.18
2006 3,068,618 80.24 19.76
2007 1,729,326 76.53 23.47
2008 1,786,781 79.63 20.37
2009 2,241,425 70.69 29.31
2010 2,299,895 68.13 31.87
2011 2,086,918 75.14 24.86
2012 2,256,096 68.86 31.14
2013 3,059,575 67.97 32.03
2014 3,111,900 61.71 38.29
2015 2,521,390 65.89 34.11

Ages

Let’s examine the most common ages of both genders rounded up by year. We are displaying their ages as of 2016, not age at which they registered. It appears 1980 (age 36) is the magic birthday for a lonely generation. At least everybody didn’t enter January 1st, 1900 like some other services.

 

Most common ages of users
Male Female
Age as of 2016 User count % of Gender Age as of 2016 User count % of Gender
36 792,989 4.02 36 680,300 8.87
31 715,042 3.62 34 529,723 6.91
30 686,519 3.48 33 443,469 5.78
34 641,739 3.25 35 373,461 4.87
32 639,203 3.24 32 364,206 4.75
29 637,477 3.23 31 349,973 4.56
33 631,956 3.2 37 331,092 4.32
35 568,919 2.88 30 295,264 3.85
28 545,021 2.76 29 261,760 3.41
37 533,961 2.71 38 243,840 3.18
38 510,229 2.59 39 227,515 2.97
46 504,684 2.56 28 191,015 2.49
26 486,069 2.46 41 152,262 1.98
41 485,805 2.46 40 145,787 1.9
39 480,231 2.43 26 139,686 1.82
27 449,015 2.28 27 139,193 1.81
40 436,800 2.21 46 131,760 1.72
42 403,677 2.05 42 113,184 1.48
25 396,853 2.01 25 112,904 1.47
44 395,844 2.01 44 109,590 1.43
47 383,617 1.94 47 106,435 1.39
43 379,590 1.92 43 105,928 1.38
45 373,854 1.89 45 105,758 1.38
51 361,097 1.83 51 102,978 1.34
48 355,945 1.8 48 101,226 1.32
49 330,965 1.68 52 98,576 1.29
56 328,898 1.67 49 97,976 1.28
50 325,135 1.65 50 97,085 1.27
52 325,103 1.65 53 92,953 1.21
53 298,192 1.51 56 89,697 1.17
54 289,756 1.47 54 88,487 1.15
55 256,856 1.3 55 80,307 1.05
22 249,164 1.26 57 70,885 0.92
24 246,207 1.25 58 66,878 0.87
23 245,612 1.24 59 62,268 0.81
21 244,634 1.24 60 56,220 0.73
57 224,512 1.14 24 53,211 0.69
58 215,073 1.09 61 53,126 0.69
59 200,978 1.02 23 49,591 0.65
20 199,717 1.01 62 45,932 0.6
61 178,304 0.9 63 39,998 0.52
60 175,545 0.89 64 35,697 0.47
62 144,966 0.73 22 35,467 0.46
63 123,391 0.63 21 34,313 0.45
64 114,735 0.58 66 32,162 0.42
66 112,973 0.57 65 30,048 0.39
65 95,101 0.48 20 26,238 0.34
68 80,643 0.41 68 25,564 0.33
67 74,553 0.38 67 24,573 0.32
69 67,384 0.34 69 21,866 0.29
70 54,162 0.27 70 18,479 0.24
71 45,616 0.23 71 15,281 0.2
72 36,208 0.18 72 12,895 0.17
73 31,270 0.16 96 12,502 0.16
74 27,416 0.14 73 11,391 0.15
76 25,739 0.13 74 10,215 0.13
75 21,505 0.11 76 8,373 0.11
77 15,647 0.08 75 8,005 0.1
78 15,242 0.08 77 6,023 0.08
79 13,716 0.07 78 5,736 0.07

Age Groups

The first table is difficult to read, so let’s group the users by age ranges of 5 years starting at 18. Over 50% of men are between 28 and 43, where over 71% of women are between the same age range. There are no shortage of news articles that propose older women lower their ages for their online dating profiles.

 

Age distribution by Gender
Age range as of 2016 Males % of Gender Females % of Gender
18 to 23 952,633 4.83 147,098 1.92
23 to 28 2,368,777 12 685,600 8.94
28 to 33 3,855,218 19.54 1,905,687 24.84
33 to 38 3,679,793 18.65 2,601,885 33.92
38 to 43 2,696,332 13.66 988,516 12.89
43 to 48 2,393,534 12.13 660,697 8.61
48 to 53 1,996,437 10.12 590,794 7.7
53 to 58 1,613,287 8.18 489,207 6.38
58 to 63 1,038,257 5.26 324,422 4.23
63 to 68 601,396 3.05 188,042 2.45
68 to 73 315,283 1.6 105,476 1.38

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