Synthetic Browsing Histories for 50 Countries Worldwide: Datasets for Research, Development, and Education

1 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata Synthetic Browsing Histories for 50 Countries Worldwide: Datasets for Research, Development, and Education Dan Komosny 1 ✉ , Saeed Ur Rehman2 & Muhammad Sohaib Ayub3 Browsing histories can be a valuable resource for cybersecurity, research, and testing. Individuals are often reluctant to share their browsing histories online, and the use of personal data requires obtaining signed informed consent. Research shows that anonymized histories can lead to re-identification, nullifying the anonymity promised by informed consent. In this work, we present 500 synthetic browsing histories valid for 50 countries worldwide. The synthetic histories are compiled based on real browsing data using a series of transformation criteria, including website content, popularity, locality, and language, ensuring their validity for the respective countries. Each history maintains the order of webpage accesses and covers a one-month period. The motivation for publishing this dataset arises from the community’s call for browsing histories from different countries for research, development, and education. The published synthetic browsing histories can be used for any purpose without legal restrictions. Background & Summary There is a common need for large public collections of browsing histories for research, education, and development. However, making a large set of real browsing histories online poses high risks of privacy breaches and data misuse. Therefore, strict legal measures are in place to ensure that personal data are protected. Informed consent must be signed by each participant, which is problematic in large numbers and internationally due to different laws. Informed consent is common to ensure the anonymity of participants. This is an empty promise as individuals can be re-identified from anonymous browsing histories. Anonymity Questioned. Su et al.1 proved that anonymous browsing histories can be used to re-identify individuals. People commonly post URL links on their social media accounts. URLs posted on a person’s X account (previously known as Twitter) were used as the recommendation set R. The authors assumed that the user had visited the links in the recommendation set before their posting online. Therefore, these URLs are also present in the anonymous user history H. In the experiment carried out, 60 simulated users were identified by their profiles in 52 % of the cases using the maximum likelihood estimate. Other methods tested were the basic intersection size and the Jaccard index. The intersection method linked a person’s history with the recommendation set by most URLs present in | R∩H |. The Jaccard index was used to eliminate the effect of the size of the recommendation set | H∩R | / | H∪R |. The methods resulted in 42 and 13 % of identified users, respectively. The results showed that the maximum likelihood estimate outperformed the simpler methods. Deußer et al.2 showed that generalization to anonymize the data does not work. The authors attempted to re-identify persons from anonymous browsing data by a limited set of URLs known for the individuals. The known URLs may come from shoulder surfing, posted links on social networks, or general knowledge of the person’s browsing behavior. The authors used data from an audience measurement provider in Germany. Over 2.5 k websites and applications were included in the data. The anonymous dataset processed contained about 4.1 M clients, 1.2 k websites and 62 k endpages. The partially known data about the individuals were the webpages visited and time. The results showed that only 2 visited URLs 1Department of Telecommunications, Brno University of Technology, Brno, Czech Republic. 2college of Science and engineering, flinders University, Adelaide, Australia. 3School of Science and engineering, Lahore University of Management Sciences, Lahore, Pakistan. ✉e-mail: k[email protected] DATA DESCRIpToR opEN 2 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ with known access time in minutes are sufficient to link about 50 % of anonymous browsing sessions to individuals. 10 visited URLs are needed to link about 80 % of anonymous browsing sessions. With the URL visit timestamp less known, for example, as an hour or day, the identification was reduced substantially. However, 7 URL visits with the known day access time were needed to identify about 20 % of the anonymous browsing sessions. Informed Consent Needed. Informed consent is used to protect user privacy. It ensures that individuals who share their personal data are aware of the purpose and risks. Nature Editorials3 stated that research studies based on personal data need informed consent. Personal data is rarely used as originally intended. The outcome of the data processing can vary as unexpected patterns are often discovered. An example is given when phone tracking records were collected. The researchers could not have asked the participants to use their data for a purpose since the purpose was not known at the time of data collection. Therefore, informed consent must be relatively open to comply with unknown purposes that may cover research, education, and development. Furthermore, any participant should be able to withdraw their personal data at any time. This is almost impossible to implement in large amounts of anonymized data, especially when already posted online. Synthetic History Applications. The synthetic browsing histories in this dataset serve as a privacy-preserving alternative to real browsing data while offering significant potential for various research and testing applications. Synthetic browsing data can be used in cybersecurity research to train and test anomaly detection systems to identify suspicious browsing and overcome privacy restrictions of real-world data4. A specific example is phishing detection, as synthetic histories provide a valuable dataset of valid benign URLs for specific countries5. The benign URLs in the synthetic histories can be used to train phishing detection models. This would allow for tailored phishing detection systems. Another usage could be for the validation of machine learning algorithms, for example, to test web crawling, as the synthetic histories provide local URLs linked to places in countries6. Synthetic histories can also provide insights into global and local web data per country, including categories such as News, Shopping, and Business1,2. On the other hand, synthetic browsing histories cannot be used for behavioral insights by examining individual browsing data, including time-of-access patterns. The reason is that the owners of synthetic histories do not exist. The virtual owners of the histories mimic the behavior of the owner of the original history. The raw data were obtained through a web crawl from Common Crawl7. That means that the web content may be corrupted, retracted, or not accessible. Custom content filtering may be applied to synthetic history entries for specific applications. Limited Synthetic Web Data. There is limited work on synthetic web usage data. Hofgesang et al.8 proposed a methodology to build synthetic web data generators based on an extensive analysis of five real-world web usage datasets. Their approach emphasized the potential of synthetic data to bridge the gap in Web Usage Mining (WUM) research, where real-world datasets are often inaccessible due to privacy concerns. Their methodology relied on real-world usage patterns to create data generators that approximate actual web interactions. This enables the development of customer profiling and personalization techniques. However, it is limited only to five websites. Our work generates a large-scale dataset of synthetic browsing histories for 50 countries. The related efforts on synthetic data focused on domain-specific applications, including biomedical for assessing machine-learning healthcare software9, finance for predictive trading strategies10, fraud detection for the case of credit cards11, and generic education cases12. Unlike these efforts, our dataset is designed to support a wide range of research fields, including web analytics, recommendation systems, cybersecurity, and ethical AI development. We are using state-of-the-art tools such as Common Crawl to ensure the dataset includes real web data while safeguarding privacy. Methods The synthetic browsing histories for 50 countries are based on a real browsing history. A series of transformation criteria are used to compile the synthetic histories for the respective country. The synthetic histories maintain the original history structure. The website access times in the synthetic histories are randomly shifted from the original history to ensure the privacy of the real history provider. The websites in the synthetic histories remain unchanged when used commonly in both the source and target countries. The website endpages in the synthetic histories are always transformed using the transformation criteria to maintain privacy. The transformation criteria used make each synthetic history valid for the target country. The compiled synthetic histories are verified for their validity in the target country. Base Algorithm. Algorithm 1 describes the compilation process of the synthetic browsing histories for the target country. The algorithm inputs are: • Original browsing history. This is a real browsing history that is used to compile the synthetic browsing histories for countries. • Common Crawl data. This data is used to transform the original history website endpages into the synthetic history website endpages. The websites and their endpages are transformed to be valid for the country. • Country and world ranked websites. The most visited websites in the world and in the target country are used to transform websites from the original history to the synthetic histories. • OpenStreetMap data. The mapping data are used to collect the local websites in the target country for the purpose of transformation of local websites, that is, websites that are not world or country ranked. • Website content detection model. This model is used to estimate the content category of the websites in the original history and in the country transformation pools. The content category is used to match the same content-category websites between the original and synthetic histories. 3 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ • Language detection model. The language detection model is used to estimate the language of the websites in the original history and in the country transformation pools. The language is used to match the equivalent websites between the original history and the synthetic histories. • Target countries. This is the list of countries for which the synthetic browsing histories are compiled. Algorithm 1 Algorithm for compilation of synthetic browsing histories for countries. 4 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ Algorithm 1 lines 3-5 are explained in detail in the section ‘Target country data pools’. Lines 8-13 and 18-23 are explained in the section ‘Data classification’. Lines 28-43 and 48-54 are explained in detail in the section ‘Synthetic history transformation’. Target Country Data pools. The country data pools contain websites and their endpages visited by users in the target country. These websites come from the country ranked websites, which are the top visited websites in the country. Country local websites are also included in the data pools. Local websites are visited by users in the country. Table1 lists the created data pools for 50 target countries. The particular data sources used are the following: • The country global websites are taken from DataForSEO13. DataForSEO provides lists of the top websites visited by users in the target countries. • The country local websites are extracted from the mapping data provided by OpenStreetMap14. Websites related to places in the target country are used. • The website endpages are compiled from Common Crawl7. Common Crawl provides extensive datasets of monthly collections from the whole Internet. The website endpages provided by Common Crawl from the same time range as the original history were used to ensure their time validity in the created country data pools. Data Classification. The classification is used for the website transformation from the original history into the synthetic history for the target country. The websites in the created country data pools and in the original history are classified for this reason. The classification process is the following: • The websites are classified into top world ranks of 10, 100, 1000, 10000, 100000, and 1000000. The top 10 rank class means that the website is present in the 10 most world visited websites. The 1000000 rank means that the website is present in the 1000000 most world visited websites. A website that is not ranked is marked as −1. Majestic Million15 is used as the source data for the world rank classification. • The websites are classified into top country ranks of 10, 100, and 500. The top 10 rank class means that the website is present in the 10 most visited websites in the country. The top 500 rank class means that the website is present in the 500 most visited websites in the country. A website that is not ranked is marked as –1. DataForSEO13 is used as the source data for the country rank classification. • The website content is classified into the following categories: Business, Generic, News, Shopping, and Society. A machine-learning model by Lugeon et al.16 is used for content classification of the websites. Minor classes are joined in major groups. For example, the classes Sports and Games are covered by the major class Society. This ensures representative records for each group. Country Websites Endpages Country Websites Endpages Argentina 126 2412 Austria 174 3433 Australia 113 2053 Belgium 162 3068 Bulgaria 133 2562 Brazil 91 1773 Canada 160 3007 Switzerland 177 3168 Chile 157 3134 Czechia 193 3858 Germany 194 3780 Denmark 170 3366 Estonia 165 3199 Egypt 118 2070 Spain 148 2815 Finland 219 4336 France 185 3708 United Kingdom 160 2996 Greece 98 1853 Croatia 216 4289 Hungary 214 4243 Ireland 172 3396 Israel 109 2024 India 165 3218 Italy 161 3141 Japan 143 2797 Sri Lanka 131 2445 Lithuania 184 3565 Latvia 145 2495 Morocco 89 1677 Mexico 141 2656 Malaysia 110 2106 Netherlands 198 3892 Norway 65 1215 New Zealand 135 2612 Pakistan 155 3061 Poland 204 4068 Portugal 136 2589 Romania 168 3270 Serbia 166 3270 Saudi Arabia 120 2083 Sweden 181 3614 Singapore 143 2694 Slovenia 176 3527 Slovakia 217 4463 Thailand 79 1456 Turkey 124 2453 Ukraine 73 1409 United States 158 3020 Vietnam 156 3069 Table 1. Country data pools for compilation of synthetic histories. 5 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ • The website language is estimated using a detection library provided by Stahl, P.17. The languages used for the target countries are the following: Hindi, Slovene, Turkish, Japanese, Bulgarian, Malay, Czech, Danish, Arabic, Nynorsk, Tamil, Polish, Thai, English, Irish, Serbian, Ukrainian, Hungarian, Slovak, Urdu, Vietnamese, Core records synthetic_time Synthetic website endpage access time synthetic_url Synthetic website endpage Extended records original_scope Original website scope original_content Original website content category original_country_rank Original website country rank original_world_rank Original website world rank original_lang Original website language original_bin Original website endpage bin Table 2. Core and extended records of synthetic browsing histories for countries. Core records form the synthetic browsing histories. Extended records are metadata of the original history. Extended record Values original_scope Global, Local original_content Business, Generic, News, Shopping, Society original_country_rank 10, 100, 500, −1 original_world_rank 10, 100, 1000, 10000, 100000, 1000000, −1 original_lang Hindi, Slovene, Turkish, Japanese, Bulgarian, Malay, Czech, Danish, Arabic, Nynorsk, Tamil, Polish, Thai, English, Irish, Serbian, Ukrainian, Hungarian, Slovak, Urdu, Vietnamese, Estonian, Swedish, Greek, Romanian, Croatian, Dutch, Latvian, Italian, Spanish, French, Finnish, Lithuanian, German, Portuguese, Hebrew original_bin 1, 25, 50, −1 Table 3. Possible values of extended synthetic history records. None values are marked as −1. Country Websites Endpages Country Websites Endpages Argentina 71 6186 Australia 84 6262 Austria 100 6283 Belgium 84 6258 Brazil 50 6125 Bulgaria 77 6332 Canada 120 6384 Chile 78 6252 Croatia 83 6104 Czechia 92 6194 Denmark 103 6308 Egypt 66 5771 Estonia 72 6041 Finland 116 5913 France 101 6172 Germany 103 5735 Greece 51 6088 Hungary 100 6336 India 101 6322 Ireland 116 6395 Israel 71 6262 Italy 91 6179 Japan 70 6244 Latvia 71 5798 Lithuania 83 6335 Malaysia 77 6414 Mexico 86 5775 Morocco 55 6082 Netherlands 121 6222 New Zealand 103 6441 Norway 39 6090 Pakistan 98 6282 Poland 104 6046 Portugal 69 5885 Romania 83 5614 Saudi Arabia 69 6205 Serbia 85 6407 Singapore 101 6299 Slovakia 81 6369 Slovenia 106 6327 Spain 73 6020 Sri Lanka 79 6127 Sweden 102 6282 Switzerland 99 6249 Thailand 40 6237 Turkey 53 6220 Ukraine 42 6034 United Kingdom 132 6392 United States 116 6439 Vietnam 64 6055 Table 4. Unique websites and total records of synthetic histories. 6 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ Estonian, Swedish, Greek, Romanian, Croatian, Dutch, Latvian, Italian, Spanish, French, Finnish, Lithuanian, German, Portuguese, and Hebrew. • The website endpages are classified into classes of 1, 25, 50 by the endpage path length. The first class is used for the root endpages of the websites. An endpage path longer than 50 is marked as –1. Synthetic History Transformation. A series of transformation criteria are used to compile the synthetic browsing histories for the target countries. The websites are first transformed, followed by their endpages. The access times in the synthetic histories are randomly shifted to mask the original history timestamps. The order of the webpage accesses is maintained as the original. Search websites in the original history are not processed, e.g., google.com. The original history is exported from a Google user account using the Google Takeout18 service available at https://takeout.google.com/settings/takeout in JSON format. Original history records are limited to a period of one month. The transformation steps are applied to the original history entries as follows: 1. Maintain the original history website if it is listed in the top country ranked websites (most visited websites) for the target country. 2. Filter websites from the same content-category in the target country pool as the original history website. 3. Filter websites from the same country rank in the target country pool as the original history website. 4. Filter websites from the same world rank category pool as the original history website. 5. Filter websites by the target country official language(s) pool or English. 6. Shift the access time of the original history endpage by a random value. The websites and their endpages access order are maintained as original. 7. Maintain the original history website if it is the root page. 8. Filter website endpages from the same path bin in the target country pool as the original website endpage. 9. Sample a result if there are more outputs from the matching criteria. Example History Record Transformation. An example transformation for a synthetic history record is presented. The original history record is https://www.bohemiapc.cz/pocitace-notebooky-prislusenstvi/komponenty/pameti/pameti-ddr5-pc/ with the access time 2024-11-29 20:42:33.421201. The target country for the transformation of the history record is Germany. The example processing steps refer to the pseudo-code lines in Algorithm 1. We do not skip lines with code structure to provide better reference to the algorithm. 1. Pseudo code line 27: This is the first line of original history transformation. The original history is iterated and the website record bohemiapc.cz is processed. 2. Pseudo code line 28: The original website bohemiapc.cz is checked for its presence in the target country ranked websites. 3. Pseudo code line 29: The condition about the website presence in the target country ranked websites is not met. The action of maintaining the original website in the synthetic history is not processed. 4. Pseudo code line 30: The original website bohemiapc.cz content category is checked. The original website content category is ‘Shopping’. There are websites with the same content category in the target country pool of Germany. 5. Pseudo code line 31: Websites with the same content category of ‘Shopping’ are filtered from the target country pool. 6. Pseudo code line 32: The original website bohemiapc.cz country rank is checked. The original website does not belong to the top visited websites in the country. It is therefore not a country ranked website. 7. Pseudo code line 33: The condition about the website presence in the top country visited websites is not met. The action of filtering websites with the same country rank from the target country pool is not processed. 8. Pseudo code line 34: Code structure line. Synthetic Synthetic Original Original Original Original Original Original Access time URL Scope Content C rank W rank Lang Path bin 2024-11-01 08:49:49.085098 https://www.gamesradar.com/age-of-empires-age-of-kingsreview/ Global Business −1 10000 eng 50 2024-11-01 09:35:46.817464 https://www.digitaltrends.com/mobile/qualcommsnapdragon-8-gen-1-plus-7-gen-1-xr/ Global Society −1 10000 eng −1 2024-11-01 09:36:17.514660 https://www.birdz.sk/forum/mala-by-sa-europa-aktivnezapojit-do-vojny-na-ukrajine/196669-tema.html Global Society 500 1000 eng −1 2024-11-29 20:06:16.230158 https://www.tescoma.sk/zapekacia-doska-na-vafle-presendvicovac-president-3-v-1-1-ks Local Shopping −1−1 ces −1 2024-11-27 09:50:00.904429 https://aukro.sk/dalekohlady Global Shopping 100 100000 ces 25 Table 5. Sample synthetic history records for Slovakia with metadata. 7 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ 9. Pseudo code line 35: The original website bohemiapc.cz world rank is checked. The original website does not belong to the top word visited websites. It is therefore not a world ranked website. 10. Pseudo code line 36: The condition about the website presence in the top world visited websites is not met. The action of filtering websites with the same world rank from the target country pool is not processed. 11. Pseudo code line 37: Code structure line. 12. Pseudo code line 38: The original website bohemiapc.cz is checked for language. The result is ‘CES’. This language does not match the target country language, which is ‘DEU’. 13. Pseudo code line 39: The condition about the website same language used in the target country is not met. The action of filtering the websites with the same language from the target country pool is not processed. 14. Pseudo code line 40: Code structure line. 15. Pseudo code line 41: The original website language is not considered and the language of the target country is used. The target country pool contains websites only with the target country languages. 16. Pseudo code line 42: Code structure line. 17. Pseudo code line 43: A website from the filtered results of the target country pool is sampled. The result is the website https://www.berndwolf.de. 18. Pseudo code line 44: Code structure line. 19. Pseudo code line 45: Code structure line. 20. Pseudo code line 46: Code structure line. 21. Pseudo code line 47: The original website endpages are iterated and the endpage pocitace-notebookyprislusenstvi/komponenty/pameti/pameti-ddr5-pc is processed. Fig. 1 Percentage of synthetic history websites in countries that belong to the top country visited websites. Original history share is 52%. Fig. 2 Empirical complementary cumulative distribution function of synthetic history records present in country top visited websites. 8 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ 22. Pseudo code line 48: The access time 2024-11-29 20:42:33.421201 is shifted by a random value to 2024-11-29 20:44:31.221766 to preserve privacy of the original history record. 23. Pseudo code line 49: The endpage pocitace-notebooky-prislusenstvi/komponenty/pameti/pameti-ddr5-pc is not a root page of the website. Country Business Generic News Shopping Society Argentina 355 205 736 1264 3626 Australia 364 203 726 1277 3692 Austria 364 214 736 1377 3592 Belgium 360 217 729 1363 3589 Brazil 323 103 145 1349 4205 Bulgaria 313 199 2350 662 2808 Canada 345 205 733 1400 3701 Chile 347 206 732 1325 3642 Croatia 327 208 1562 1299 2708 Czechia 1698 212 149 1339 2796 Denmark 360 213 720 1376 3639 Egypt 343 212 1819 246 3151 Estonia 264 206 1499 1328 2744 Finland 324 186 1348 1284 2771 France 364 217 1727 323 3541 Germany 367 205 737 1090 3336 Greece 323 1510 149 1277 2829 Hungary 365 217 692 1412 3650 India 326 206 1585 1340 2865 Ireland 363 219 731 1369 3713 Israel 710 622 148 293 4489 Italy 368 187 728 1369 3527 Japan 484 372 270 296 4822 Latvia 319 206 1553 1347 2373 Lithuania 338 216 718 1399 3664 Malaysia 367 210 733 1377 3727 Mexico 362 199 736 1132 3346 Morocco 1017 171 1049 211 3634 Netherlands 365 213 711 1319 3614 New Zealand 637 743 731 334 3996 Norway 1384 214 1550 314 2628 Pakistan 358 213 738 1263 3710 Poland 327 196 1217 1502 2804 Portugal 364 195 1645 293 3388 Romania 330 210 149 2626 2299 Saudi Arabia 547 680 1123 323 3532 Serbia 360 205 707 1414 3721 Singapore 366 214 720 1354 3645 Slovakia 763 199 1152 1418 2837 Slovenia 365 208 731 1410 3613 Spain 758 498 467 191 4106 Sri Lanka 1405 123 727 320 3552 Sweden 363 211 689 1408 3611 Switzerland 367 197 735 1332 3618 Thailand 872 1667 674 198 2826 Turkey 318 1611 147 1399 2745 Ukraine 993 210 432 319 4080 United Kingdom 369 214 726 1389 3694 United States 368 216 734 1386 3735 Vietnam 329 193 1556 1271 2706 Table 6. Distribution of website content categories in synthetic histories. 9 Scientific Data | (2025) 12:128 | https://doi.org/10.1038/s41597-025-04407-z www.nature.com/scientificdata www.nature.com/scientificdata/ 24. Pseudo code line 50: The condition about the root page is not met. The action of maintaining the endpage is not processed. 25. Pseudo code line 51: The original endpage pocitace-notebooky-prislusenstvi/komponenty/pameti/pametiddr5-pc is checked for the path bin. The result is –1. There are websites with the same path bin in the target country pool. 26. Pseudo code line 52: Endpages with the same path bin are filtered from the target country pool. 27. Pseudo code line 53: Code structure line. 28. Pseudo code line 54: An endpage from the filtered results in the target country pool is sampled. The result endpage is antoinette-ketten-mit-anhaenger-sterlingsilber-925-granat-rot. The final transformation result for the original history record https://www.bohemiapc.cz/pocitace-notebookyprislusenstvi/komponenty/pameti/pameti-ddr5-pc with the access time 2024-11-29 20:42:33.421201 is the synthetic history record https://www.berndwolf.de/antoinette-ketten-mit-anhaenger-sterlingsilber-925-granat-rot with access time 2024-11-29 20:44:31.221766 valid for Germany. Data Records The synthetic browsing histories are deposited on Figshare19. Table2 describes the records of the published synthetic browsing histories. The core records are the access time and the URL. The extended records are the metadata of the original history used to compile the synthetic history record. Table3 gives an overview of the values of each record. Table4 shows the number of synthetic history records in the target countries. There are 10 synthetic histories compiled for each country. Table5 shows sample values of a synthetic history for Slovakia. Extended samples of synthetic histories per country are available on GitHub20. Extended samples are present for each of 500 synthetic histories. Fig. 3 Empirical complementary cumulative distribution function of transformed original history records. Fig. 4 Successfully transformed synthetic history records in countries.