Preface
Badoo.com was hacked. LeakedSource has obtained and added a copy of this data to its ever-growing searchable repository of leaked data. Badoo has not returned our request for comment on this matter but they did respond to a reporter named Joseph Cox with a denial of the breach. This reporter independently verified the breach.
LeakedSource is a search-engine capable of searching over 1.7 billion leaked records — an aggregation of data from hundreds of disparate sources. We have been able to accumulate this data over a relatively short period of time through a combination of deep-web scavenging and rumor-chasing. Occasionally these efforts lead to major discoveries, but we really aren’t too picky. If we come across a leaked database from a company that most people haven’t heard of, we will incorporate it into our master database just the same.
You may search for yourself in the leaked Badoo.com database by visiting our homepage. If your personal information appears in our copy of this database, or in any other leaked database that we possess, you may remove yourself for free. We’re rebuilding our removal tools to be automated and more efficient so check back later.
Since embarking on this ambitious project just a handful of months ago, we have processed an unbelievable amount of data. Much more than we expected, more than most large companies will ever house — and we’re just getting started. LeakedSource may soon become synonymous with Big Data, so don’t miss out!
Anyone may use the information on this page for free in any capacity provided LeakedSource is given credit and a link back.
LeakedSource does not engage in, encourage or condone unlawful entry (“hacking”) into private systems.
Summary
This data set contains 127,343,437 records. Each record may contain up to an email address, a username, password, gender, first and last name, birthday, and some other unknown integer values.
API
After the last breach we received many requests for API access, and we are launching a business API with a consumer one to follow in the near future. You can read about the API features at our API page
Passwords
Passwords were stored in MD5 with no salting. “Salting” makes decrypting passwords exponentially harder when dealing with large numbers of passwords such as these. The methods Badoo used for storing passwords are not what internet standards propose and is very weak encryption or some would say it’s not encryption at all because it’s so outdated.
The following table shows the top passwords used by Badoo users.
Rank | Password | Frequency |
1 | 123456 | 706,689 |
2 | 123456789 | 237,898 |
3 | 12345 | 107,211 |
4 | 000000 | 78,924 |
5 | 111111 | 62,445 |
6 | 12345678 | 61,658 |
7 | azerty | 56,688 |
8 | paSSword | 54,128 |
9 | 1234567 | 53,003 |
10 | badoo | 49,918 |
11 | 123123 | 37,082 |
12 | 1234567890 | 33,945 |
13 | 654321 | 28,728 |
14 | qwerty | 25,736 |
15 | 666666 | 25,000 |
16 | juventus | 23,659 |
17 | antonio | 21,679 |
18 | andrea | 21,153 |
19 | 121212 | 19,960 |
20 | 010203 | 18,632 |
21 | 987654321 | 18,590 |
22 | marseille | 18,052 |
23 | 222222 | 17,177 |
24 | 112233 | 17,149 |
25 | password | 17,098 |
26 | 555555 | 16,540 |
27 | napoli | 16,493 |
28 | loulou | 16,189 |
29 | amoremio | 15,949 |
30 | 11111 | 15,898 |
31 | francesco | 15,680 |
32 | giuseppe | 15,575 |
33 | 00000 | 15,534 |
34 | doudou | 15,124 |
35 | barcelona | 14,060 |
36 | samsung | 13,945 |
37 | 101010 | 13,300 |
38 | stella | 13,296 |
39 | 102030 | 13,219 |
40 | aaaaaa | 12,854 |
41 | ciaociao | 12,786 |
42 | alessandro | 12,732 |
43 | 123321 | 12,704 |
44 | 789456 | 12,644 |
45 | valentina | 12,456 |
46 | amore | 12,261 |
47 | 123654 | 12,210 |
48 | 7777777 | 11,929 |
49 | soleil | 11,917 |
50 | chouchou | 11,835 |
51 | 696969 | 11,724 |
52 | daniel | 11,661 |
53 | stellina | 11,589 |
54 | mercedes | 11,345 |
55 | francesca | 11,295 |
Emails
Simple table of top email domains
Rank | Email Domain | Frequency |
1 | @hotmail.com | 37,273,584 |
2 | @hotmail.fr | 11,030,772 |
3 | @gmail.com | 9,629,547 |
4 | @yahoo.com | 7,269,404 |
5 | @hotmail.it | 5,493,132 |
6 | @yahoo.fr | 3,090,199 |
7 | @live.fr | 3,085,478 |
8 | @mail.ru | 2,658,804 |
9 | @libero.it | 2,451,448 |
10 | none | 2,052,344 |
11 | @seznam.cz | 2,000,361 |
12 | @live.it | 1,926,094 |
13 | @wp.pl | 1,781,118 |
14 | @hotmail.es | 1,540,475 |
15 | @hotmail.de | 1,453,651 |
16 | @web.de | 1,305,301 |
17 | @yahoo.it | 1,165,831 |
18 | @msn.com | 1,094,808 |
19 | @hotmail.co.uk | 1,020,359 |
20 | @live.com | 993,910 |
21 | @facebook.com | 975,775 |
22 | @gmx.de | 879,061 |
23 | @orange.fr | 823,800 |
24 | @alice.it | 771,721 |
25 | @o2.pl | 764,257 |
26 | @yahoo.es | 646,944 |
27 | @yandex.ru | 549,091 |
28 | @yahoo.de | 538,597 |
29 | @freemail.hu | 538,308 |
30 | @yahoo.co.uk | 510,490 |
31 | @tiscali.it | 491,761 |
32 | @interia.pl | 483,671 |
33 | @live.nl | 411,557 |
34 | @wanadoo.fr | 391,419 |
35 | @virgilio.it | 379,132 |
36 | @aol.com | 376,951 |
37 | @ymail.com | 375,295 |
38 | @live.de | 369,323 |
39 | @free.fr | 366,341 |
40 | @laposte.net | 343,131 |
41 | @windowslive.com | 327,105 |
42 | @googlemail.com | 326,907 |
43 | @op.pl | 322,224 |
44 | @rambler.ru | 291,796 |
45 | @citromail.hu | 256,129 |
46 | @live.co.uk | 246,604 |
47 | @centrum.cz | 243,614 |
48 | @naver.com | 232,595 |
49 | @t-online.de | 228,743 |
50 | @sfr.fr | 223,587 |
51 | @gmx.net | 207,956 |
52 | @bk.ru | 200,257 |
53 | @live.com.pt | 199,734 |
54 | @freenet.de | 188,232 |
55 | @neuf.fr | 183,988 |
Genders
Gender ratios on dating sites are always of interest. Here is how the Badoo.com user base is split (not all users had a gender in the data set). The amount of female users is far above what we expected to see from similar sites such as Mate1.com
Rank | Gender | Frequency | % of users |
1 | Male | 72,810,668 | 57.18 |
2 | Female | 52,456,844 | 41.19 |
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.
Most common ages of users | |||||
Male | Female | ||||
Age as of 2016 | User count | % of Gender | Age as of 2016 | User count | % of Gender |
26 | 3,495,628 | 4.8 | 26 | 3,152,682 | 6.01 |
36 | 3,302,195 | 4.54 | 27 | 2,688,924 | 5.13 |
28 | 3,208,226 | 4.41 | 28 | 2,602,815 | 4.96 |
31 | 3,189,339 | 4.38 | 25 | 2,352,485 | 4.48 |
27 | 3,072,355 | 4.22 | 29 | 2,283,996 | 4.35 |
29 | 3,060,087 | 4.2 | 30 | 2,243,709 | 4.28 |
30 | 3,041,413 | 4.18 | 31 | 2,236,743 | 4.26 |
32 | 2,928,620 | 4.02 | 32 | 1,980,620 | 3.78 |
34 | 2,759,516 | 3.79 | 36 | 1,969,260 | 3.75 |
33 | 2,732,729 | 3.75 | 33 | 1,835,760 | 3.5 |
25 | 2,500,823 | 3.43 | 34 | 1,804,584 | 3.44 |
35 | 2,449,389 | 3.36 | 24 | 1,719,884 | 3.28 |
37 | 2,168,972 | 2.98 | 35 | 1,588,524 | 3.03 |
38 | 2,092,085 | 2.87 | 37 | 1,270,038 | 2.42 |
39 | 1,956,904 | 2.69 | 23 | 1,222,535 | 2.33 |
24 | 1,856,107 | 2.55 | 38 | 1,195,681 | 2.28 |
41 | 1,839,607 | 2.53 | 39 | 1,120,530 | 2.14 |
40 | 1,768,010 | 2.43 | 41 | 1,002,860 | 1.91 |
42 | 1,529,430 | 2.1 | 40 | 994,812 | 1.9 |
46 | 1,496,181 | 2.05 | 22 | 932,186 | 1.78 |
43 | 1,368,000 | 1.88 | 42 | 873,046 | 1.66 |
44 | 1,338,185 | 1.84 | 46 | 830,998 | 1.58 |
23 | 1,306,429 | 1.79 | 43 | 789,860 | 1.51 |
45 | 1,169,900 | 1.61 | 44 | 770,409 | 1.47 |
47 | 1,039,161 | 1.43 | 45 | 691,064 | 1.32 |
48 | 964,210 | 1.32 | 47 | 627,457 | 1.2 |
22 | 916,121 | 1.26 | 48 | 586,025 | 1.12 |
49 | 857,548 | 1.18 | 49 | 532,300 | 1.01 |
50 | 831,315 | 1.14 | 50 | 506,434 | 0.97 |
51 | 822,377 | 1.13 | 51 | 500,105 | 0.95 |
52 | 705,764 | 0.97 | 52 | 436,528 | 0.83 |
53 | 597,530 | 0.82 | 21 | 400,067 | 0.76 |
56 | 552,616 | 0.76 | 53 | 381,964 | 0.73 |
54 | 546,436 | 0.75 | 20 | 350,741 | 0.67 |
55 | 464,130 | 0.64 | 54 | 342,395 | 0.65 |
57 | 362,904 | 0.5 | 56 | 334,825 | 0.64 |
58 | 333,850 | 0.46 | 55 | 302,383 | 0.58 |
21 | 316,425 | 0.43 | 57 | 241,207 | 0.46 |
59 | 295,459 | 0.41 | 58 | 220,490 | 0.42 |
60 | 263,216 | 0.36 | 19 | 209,321 | 0.4 |
61 | 254,608 | 0.35 | 59 | 192,527 | 0.37 |
20 | 223,709 | 0.31 | 60 | 172,195 | 0.33 |
62 | 202,429 | 0.28 | 61 | 162,300 | 0.31 |
63 | 170,211 | 0.23 | 62 | 130,757 | 0.25 |
64 | 156,173 | 0.21 | 63 | 111,012 | 0.21 |
66 | 150,869 | 0.21 | 64 | 98,722 | 0.19 |
65 | 130,236 | 0.18 | 66 | 90,963 | 0.17 |
19 | 119,649 | 0.16 | 65 | 83,295 | 0.16 |
67 | 100,719 | 0.14 | 18 | 63,779 | 0.12 |
68 | 92,257 | 0.13 | 67 | 61,803 | 0.12 |
69 | 80,090 | 0.11 | 104 | 57,665 | 0.11 |
70 | 67,550 | 0.09 | 68 | 55,581 | 0.11 |
18 | 58,654 | 0.08 | 105 | 51,141 | 0.1 |
71 | 56,329 | 0.08 | 69 | 47,464 | 0.09 |
72 | 44,303 | 0.06 | 103 | 41,030 | 0.08 |
73 | 35,816 | 0.05 | 70 | 39,185 | 0.07 |
74 | 30,231 | 0.04 | 106 | 35,751 | 0.07 |
76 | 28,969 | 0.04 | 71 | 30,875 | 0.06 |
75 | 24,683 | 0.03 | 72 | 23,943 | 0.05 |
77 | 18,921 | 0.03 | 73 | 19,300 | 0.04 |
Age Groups
The first table is difficult to read, so let’s group the users by age ranges of 5 years starting at 18.
Age distribution by Gender | ||||
Age range as of 2016 | Males | % of Gender | Females | % of Gender |
18 to 23 | 2,940,987 | 4.04 | 3,178,629 | 6.06 |
23 to 28 | 15,439,568 | 21.21 | 13,739,325 | 26.19 |
28 to 33 | 18,160,414 | 24.94 | 13,183,643 | 25.13 |
33 to 38 | 15,504,886 | 21.29 | 9,663,847 | 18.42 |
38 to 43 | 10,554,036 | 14.5 | 5,976,789 | 11.39 |
43 to 48 | 7,375,637 | 10.13 | 4,295,813 | 8.19 |
48 to 53 | 4,778,744 | 6.56 | 2,943,356 | 5.61 |
53 to 58 | 2,857,466 | 3.92 | 1,823,264 | 3.48 |
58 to 63 | 1,519,773 | 2.09 | 989,281 | 1.89 |
63 to 68 | 800,465 | 1.1 | 501,376 | 0.96 |
68 to 73 | 376,345 | 0.52 | 216,348 | 0.41 |