CN106650382A - Browser-based high-performance user tracking method - Google Patents
Browser-based high-performance user tracking method Download PDFInfo
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- CN106650382A CN106650382A CN201611251875.4A CN201611251875A CN106650382A CN 106650382 A CN106650382 A CN 106650382A CN 201611251875 A CN201611251875 A CN 201611251875A CN 106650382 A CN106650382 A CN 106650382A
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- Prior art keywords
- browser
- fingerprint
- information
- characteristic information
- value
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- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
Abstract
The invention discloses a browser-based high-performance user tracking method which comprises the following steps: 1) acquiring user browser characteristic information, generating a corresponding Fingerprint value and sending the user browser characteristic information and the corresponding Fingerprint value to a corresponding background server; 2) performing relevant treatment on the acquired corresponding data on the basis of a strategic fingerprint recognition method according to the user browser characteristic information and the corresponding Fingerprint value; and 3) storing the related user client information in a Redis database, adopting a filtering recognition mode for treating the new fingerprint data and judging whether the two fingerprint information are relevant to each other. According to the technical scheme provided by the invention, the user can be conveniently and efficiently tracked.
Description
Technical field
The invention belongs to information gathering techniques field, more particularly to a kind of high-performance user tracking side based on browser
Method.
Background technology
As the safety problem of privacy of user awareness of safety more and more higher, and Cookie leakages cruelly is also increased, user
Individual privacy is protected by installing the method such as plug-in unit or disabling Cookie.Although Canvas fingerprint techniques are effectively to chase after
One user of track, because Canvas Renderings are based on the hardware device of user, so the technology has a very high collision
Rate.For some malicious attackers, by way of VPS or other encryption agents the information of oneself is hidden so that follow the trail of
Become more difficult with identification.The mode of traditional analysis daily record can only be analyzed to attack, can not effectively chase after
Track malicious attacker, the tracking and identification of user is still very difficult.The Browser Fingerprint fingerprints that EFF is proposed are known
Other technology effectively and can uniquely identify the browser characteristic information of user, but convenient not efficient user is followed the trail of.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of high-performance user method for tracing based on browser.
To solve the above problems, the present invention is adopted the following technical scheme that：
A kind of high-performance user method for tracing based on browser is comprised the following steps：
Step 1, user browser characteristic information is obtained, generate corresponding Fingerprint fingerprint values, and by the user
Browser characteristic information and corresponding Fingerprint fingerprint values are sent to corresponding background server；
Step S2, according to the user browser characteristic information and corresponding Fingerprint fingerprint values, according to strategy
Property fingerprint identification method is associated process to the corresponding data for obtaining；
Step S3, by the subscription client information Store after association to Redis databases, using filtering RM to new
Finger print data processed, judge two finger print informations whether be associated.
Preferably, the information of user browser feature includes that the characteristic information and browser of browser itself are located eventually
The information at end；The characteristic information of browser itself includes：Browser UA, language Language, using font Font, install plug-in unit
Locally stored mechanism Local_ of platform Platform, HTML5 belonging to Plugins, Cookie and Canvas information, browser
Stroage and Session_stroage, whether allow to follow the trail of Do Not Track etc.；The information of browser place terminal includes：
Public network and Intranet IP；Color depth Color_depth；Time zone Time_zone；Screen resolution Resolution etc..
Preferably, by tri- groups of fingerprint values, cookie and canvas, Intranet IP and outer net IP in step S2
The mode of unique mark user is come the contrast judgement to being associated property of finger print information.
It is first that it is identified with redis databases when there is new finger print information preferably, in step S3
Fingerprint is compared, and if existing corresponding database can be directly stored in, and with all fingerprints strategy is carried out again if not existing
Property identification association compare.
If preferably, can not be by tri- groups of fingerprint values, cookie and canvas, Intranet IP and outer net IP
The mode of unique mark user then needs to carry out remaining characteristic information come the contrast judgement to being associated property of finger print information
Further discriminant analysis, specially：Shorter for characteristic information value length directly carries out character string comparison；Use
Difflib storehouses it is longer to characteristic information value length carry out the character string likelihood ratio compared with by the ratio () function in Difflib storehouses
The Similarity value of characteristic information value is tried to achieve, if the Similarity value is in the corresponding threshold range of feature, then it is assumed that characteristic information phase
Together；It is multiplied with respective weights value with reference to result of the comparison is calculated, the final fingerprint Similarity value of two fingerprints can be drawn；Will be described
Fingerprint Similarity value is compared with the similarity threshold of browser fingerprint, and the fingerprint Similarity value in threshold range then shows
Two fingerprints representated by it come from same browser.
Preferably, giving each browser characteristic information setting weight using Delphi method, final weighted value is drawn.
The present invention collects the information of user browser feature to realize effectively following the trail of user, and in large batch of user
Efficiently identify in browser characteristic information because browser characteristic information changes the different Fingerprint values for generating, together
The recognition methods of Shi Caiyong tactics, relies on a website built in public network server, main to be responsible for collecting use
The browser characteristic information at family, is effectively associated and is recognized, to realize high-performance to the fingerprint for having change of different phase
User tracking.
User tracking method of the present invention, the browser characteristic information of user in being collected by website, and send to website
Platform；The data collected are associated with analysis using tactic recognizer, the tactic recognizer of the present invention uses moral
You give each browser feature-set weight at Philippine side method；In the comparison of characteristic information, the character string shorter to characteristic information value
Directly compared, similitude comparison is carried out to longer characteristic information value character string by Difflib, for same browser
Change in presumable scope in the characteristic information value for comparing carries out qualitative test, and with this corresponding threshold of each item is tried to achieve
Value, by the ratio () function in Difflib storehouses the Similarity value of characteristic information value is tried to achieve, if the Similarity value is in feature correspondence
Threshold range in, then it is assumed that characteristic information is identical；It is multiplied with respective weights value with reference to result of the comparison is calculated, two can be drawn
The final fingerprint Similarity value of fingerprint；All of browser characteristic information item is finally directed to, is defined less than two (comprising two)
Characteristic information value it is inconsistent for same browser fingerprint, and all of browser information value is qualitatively upgraded or is forged
All fingerprint Similarity values for measuring are averaged by test, are set as the similarity threshold of whole browser fingerprint；By upper one
Step the fingerprint Similarity value tried to achieve and the similarity threshold for obtaining are compared, the then table of the fingerprint Similarity value in threshold range
Bright two fingerprints representated by it come from same browser；When new fingerprint is collected, by the fingerprint for having identified
In carry out filtration identification, if not matching, being continuing with tactic recognizer and all of fingerprint carries out analogous relationship point
Analysis.The present invention can realize efficiently judging whether two finger print informations are associated.
Description of the drawings
Fig. 1 is to calculate the result schematic diagram of weight based on Delphi method in embodiment of the present invention；
Fig. 2 is the displaying schematic diagram of some the browser characteristic item information in embodiment of the present invention；
Fig. 3 is the core alignment algorithm in embodiment of the present invention in tactic recognizer；
Fig. 4 is the block schematic illustration of user tracking in embodiment of the present invention.
Specific embodiment
The present invention is described in further detail below with reference to specific embodiment shown in the drawings.
The embodiment of the present invention provides a kind of high-performance user method for tracing based on browser, comprises the following steps：
Step 1, user browser characteristic information is obtained, generate corresponding Fingerprint fingerprint values, and by the user
Browser characteristic information and corresponding Fingerprint fingerprint values are sent to corresponding background server；
Step S2, according to the user browser characteristic information and corresponding Fingerprint fingerprint values, according to strategy
Property fingerprint identification method is associated process to the corresponding data for obtaining；
Step S3, by the subscription client information Store after association to Redis databases, using filtering RM to new
Finger print data processed, judge two finger print informations whether be associated.
Step 1 is specially：
Using the characteristic information of JavaScript language technical limit spacing user browser.The packet of user browser feature
Include the characteristic information of browser itself and the information of browser place terminal；The characteristic information of browser itself includes：Browse
Device UA, language Language, using font Font, plug-in unit Plugins, Cookie and Canvas information is installed, belonging to browser
Platform Platform, HTML5 locally stored mechanism Local_stroage and Session_stroage, whether allow follow the trail of
Do Not Track etc.；The information of browser place terminal includes：Public network and Intranet IP；Color depth Color_depth；Time zone
Time_zone；Screen resolution Resolution etc..All it is written in regard to the collecting function of browser characteristic information
In Fingerprint.js scripts.
By building test website on VPS, information function script is loaded on website.When user browses the net
When station, JavaScript scripts are collected to browser characteristic information, using murmurhash algorithms to characteristic information
Carry out Hash and generate a fingerprint value, i.e. browser fingerprint.Website front-end to background server, and is stored in all information backs
Database.
Step 2 is specially：
Analysis, whole association process are associated to all user browser finger print informations for receiving in background server
Including：
Weight is set using Delphi method
Because the different characteristic of browser differs to the impact that finger prints processing is caused, it is therefore desirable to give each follow-up phase of participation
The browser feature-set weight for seemingly comparing, these significance level is carried out it is rationed, the present invention utilize Dare Philippine side
Method gives each browser characteristic information setting weight, specially：The feature of browser is given a mark according to Web security experts,
And each is fed back and is inquired into after the completion of first round marking, the marking of next round is then carried out again, given a mark by several wheels
Afterwards, the statistical information of user data of the mean value for a few wheel marking being obtained with EFF to collecting is combined carries out meter of averaging
Calculate, obtain final value and be weighted value.
Tactic recognizer
Two finger print informations are associated with analysis, the fingerprint values to being generated by characteristic information first compare
Right, fingeprint values have uniqueness, if two values are equal, assert that two fingerprints belong to same user.If
Fingerprint values are unequal, and using Cookie and Canvas, two combinations of Intranet IP and outer net IP are judged fingerprint,
Because whether the value of two combinations is respectively provided with uniqueness, it is possible thereby to judge two fingerprints from same client.If with
On can not differentiate successfully, then need to carry out further discriminant analysis to remaining characteristic information.
The newly-built list of finger print information to comparing per two, for the shorter item of value of information length in browser feature
(such as the Platform items in Fig. 2), is compared using the method for direct character string comparison, the phase if equal, in list
Position is answered to put numerical value 1, if, for 0.The characteristic information value (such as User Agent item in Fig. 2) longer to length,
Regular Plugins and Fonts etc., then compared using Difflib storehouses, for same browser in the feature for comparing
Change in the value of information in presumable scope carries out qualitative test, and with this corresponding threshold value of each item is tried to achieve, if two spies
The value that reference breath compares, i.e. the ratio () functional value in Difflib storehouses then think not less than the corresponding threshold value of this feature information
Equal, corresponding positions put 0 in list, otherwise for 1；The tabular form for finally obtaining is [1,0,1,1 ...].Compare with reference to calculating
Result be multiplied with respective weights value, the final fingerprint Similarity value of two fingerprints can be drawn；Finally it is directed to all of browser
Characteristic information item, the characteristic information value for defining less than two (comprising two) is inconsistent for same browser fingerprint, and to owning
Browser information value qualitatively upgraded or forged test, all fingerprint Similarity values for measuring are averaged, set
For the similarity threshold of whole browser fingerprint；The fingerprint Similarity value that previous step is tried to achieve is carried out with the similarity threshold for obtaining
Compare, the fingerprint Similarity value in threshold range then shows that two fingerprints representated by it come from same browser.
Step S3 is specially：
All finger print informations for having associated are stored in redis databases, and by fingerprint values, cookie
Several classification such as value and IP are stored.When there is new finger print information, first it is entered with fingerprint identified in redis
Row is compared, and if existing corresponding database can be directly stored in, and is compared with all fingerprints again if not existing.
As shown in figure 1, in embodiments of the present invention, in the user browser mark sheet of collection shown in Fields.It is logical
Cross carries out Hash operation to browser characteristic item using murmurhash methods, generates unique fingerprint values.
Using Delphi method, by specific expert the feature of all browsers is given a mark according to set program,
After the completion of first round marking, AC regeneration is carried out to each expert.Then the feature marking of the second wheel is carried out, and so on
Several wheels, the suggestion for making panel of expert tends to concentrating.Browser feature is changed with reference to electronics outpost's foundation (EFF) with regard to user
Data analysis, try to achieve the mean value of expert analysis mode and EFF data evaluations, draw the weighted value of last feature.
Fig. 2 illustrates the information of the several characteristic items in browser fingerprint, it is possible to use these information are to different fingerprints
Information is associated analysis.
The fingerprint values of two finger print informations are compared first, because fingerprint values are unique,
If both are equal, then it is assumed that two fingerprints are same clients.Otherwise continue to be compared cookie, cookie also has
Have uniqueness, can be judged as the unique mark of user, think if two cookie values are equal fingerprint from
Same client.On the premise of user disabling cookie, it is compared using canvas as unique mark, canvas refers to
Although line has high collision rate, but the state with reference to cookie still can compare as unique mark.
If above two uniquely identifieds compare still different, compare with reference to intranet and extranet IP, i.e. publicIP
And intranetIP.User may be browsed web sites by configuring VPN or encryption agents, so publicIP cannot function as user
Unique identifier.WebRTC leaks can cruelly leak Intranet IP of user, and its function is equivalent to ipconfig/all orders.User
Network environment differ widely, so the combination of intranet and extranet IP has uniqueness.
Fig. 3 illustrate more than judge fail in the case of, using remaining feature (i.e. characteristic item in Fig. 1) to two
Bar fingerprint carries out the false code of analogous relationship analysis.
Before comparison the result after newly-built list storage relatively, shorter for the information character string length of feature
Item, using the method for direct character string comparison.If two character strings are equal, value 1 is put into list corresponding position,
Otherwise the position I 0.The item longer for the information character string of feature, is used for difflib modules and is compared, and each is special
Levy and have the threshold value of oneself, if the value that the item difflib of two fingerprints is tried to achieve is less than corresponding threshold value, relevant position is 0,
Otherwise it is 1.
As shown in figure 4, the experimental situation of the present invention is to build in the website of public network, user when browsing web sites, its
Browser characteristic information is collected and is sent to website backstage and is deposited into MySQL database, is associated by tactic recognizer
The finger print information of analysis is deposited into Redis databases by feature, when new finger print information is transferred to backstage, fresh information can first and
The fingerprint being stored in Redis is compared, that is, filter identification.If there is no equal item, then with institute in MySQL database
Some finger print informations are associated judgement.
Above example is only the exemplary embodiment of the present invention, is not used in the restriction present invention, protection scope of the present invention
It is defined by the claims.Those skilled in the art can make respectively in the essence and protection domain of the present invention to the present invention
Modification or equivalent are planted, this modification or equivalent also should be regarded as being within the scope of the present invention.
Claims (6)
1. a kind of high-performance user method for tracing based on browser, it is characterised in that comprise the following steps：
Step 1, acquisition user browser characteristic information, generate corresponding Fingerprint fingerprint values, and the user is browsed
Device characteristic information and corresponding Fingerprint fingerprint values are sent to corresponding background server；
Step S2, according to the user browser characteristic information and corresponding Fingerprint fingerprint values, refer to according to tactic
Line recognition methods is associated process to the corresponding data for obtaining；
Step S3, by the subscription client information Store after association to Redis databases, using filtering RM to new finger
Line data are processed, and judge whether two finger print informations are associated.
2. the high-performance user method for tracing of browser is based on as claimed in claim 1, it is characterised in that user browser is special
The information levied includes the characteristic information of browser itself and the information of browser place terminal；The characteristic information of browser itself
Including：Browser UA, language Language, using font Font, plug-in unit Plugins, Cookie and Canvas information, clear is installed
Locally stored mechanism Local_stroage and Session_stroage of platform Platform, HTML5 for looking at belonging to device, whether
Allow to follow the trail of DoNotTrack etc.；The information of browser place terminal includes：Public network and Intranet IP；Color depth Color_
depth；Time zone Time_zone；Screen resolution Resolution etc..
3. the high-performance user method for tracing of browser is based on as claimed in claim 1, it is characterised in that passed through in step S2
Fingerprint values, cookie and canvas, the mode of tri- groups of unique mark users of Intranet IP and outer net IP are come to finger print information
The contrast judgement of being associated property.
4. the high-performance user method for tracing of browser is based on as claimed in claim 1, it is characterised in that in step S3, when
There is new finger print information, first it compares with fingerprint identified in redis databases, can directly deposit if existing
Enter corresponding database, carry out with all fingerprints tactic identification again if not existing and associate to compare.
5. the high-performance user method for tracing of browser is based on as claimed in claim 1, it is characterised in that if can not pass through
Fingerprint values, cookie and canvas, the mode of tri- groups of unique mark users of Intranet IP and outer net IP are come to finger print information
The contrast judgement of being associated property, then need to carry out further discriminant analysis to remaining characteristic information, specially：For spy
Levy value of information length it is shorter directly carry out character string comparison；Using Difflib storehouses carry out longer to characteristic information value length
The character string likelihood ratio by the ratio () function in Difflib storehouses compared with trying to achieve the Similarity value of characteristic information value, if the similarity
Value is in the corresponding threshold range of feature, then it is assumed that characteristic information is identical；With reference to calculating result of the comparison and respective weights value phase
Take advantage of, the final fingerprint Similarity value of two fingerprints can be drawn；By the fingerprint Similarity value and the similarity threshold of browser fingerprint
Value is compared, and the fingerprint Similarity value in threshold range then shows that two fingerprints representated by it come from same browsing
Device.
6. the high-performance user method for tracing of browser is based on as claimed in claim 5, it is characterised in that using Dare Philippine side
Method gives each browser characteristic information setting weight, draws final weighted value.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107508832A (en) * | 2017-09-21 | 2017-12-22 | 深圳智盾信息技术有限公司 | A kind of device-fingerprint recognition methods and system |
| CN108171074A (en) * | 2017-12-07 | 2018-06-15 | 东南大学 | One kind is based on the associated Web trackings automatic testing method of content |
| CN109104456A (en) * | 2018-06-07 | 2018-12-28 | 北京本邦科技股份有限公司 | A kind of user tracking based on browser fingerprint and propagating statistics analysis method |
| CN109587133A (en) * | 2018-11-30 | 2019-04-05 | 武汉烽火众智智慧之星科技有限公司 | A kind of single-node login system and method |
| CN109635225A (en) * | 2018-12-14 | 2019-04-16 | 平安城市建设科技(深圳)有限公司 | Track method, apparatus, server and the storage medium of browser information |
| CN109688210A (en) * | 2018-12-14 | 2019-04-26 | 平安城市建设科技(深圳)有限公司 | Track method, apparatus, server and the storage medium of user information |
| CN109995576A (en) * | 2019-02-13 | 2019-07-09 | 平安科技（深圳）有限公司 | Recognition methods, device and the storage medium of equipment for surfing the net, computer equipment |
| CN110505231A (en) * | 2019-08-27 | 2019-11-26 | 北京丁牛科技有限公司 | One kind going anonymization tracing system and method |
| CN110866286A (en) * | 2019-10-29 | 2020-03-06 | 武汉极意网络科技有限公司 | Equipment fingerprint generation method and device |
| CN111698082A (en) * | 2020-05-29 | 2020-09-22 | 成都新希望金融信息有限公司 | Method for generating fingerprint identification of hybrid terminal equipment based on JS |
| CN113067802A (en) * | 2021-03-12 | 2021-07-02 | 京东数字科技控股股份有限公司 | User identification method, device, equipment and computer readable storage medium |
| CN114943024A (en) * | 2022-05-31 | 2022-08-26 | 北京永信至诚科技股份有限公司 | Fingerprint acquisition method and device based on browser |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103166917A (en) * | 2011-12-12 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Method and system for network equipment identity recognition |
| CN103246701A (en) * | 2013-04-02 | 2013-08-14 | 百度在线网络技术（北京）有限公司 | Quick search method, system and device |
| CN105577651A (en) * | 2015-12-16 | 2016-05-11 | 广州酷狗计算机科技有限公司 | Service providing method and apparatus |
| CN106230831A (en) * | 2016-05-31 | 2016-12-14 | 众安在线财产保险股份有限公司 | A kind of method and system identifying browser uniqueness and feature of risk |
-
2016
- 2016-12-30 CN CN201611251875.4A patent/CN106650382A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103166917A (en) * | 2011-12-12 | 2013-06-19 | 阿里巴巴集团控股有限公司 | Method and system for network equipment identity recognition |
| CN103246701A (en) * | 2013-04-02 | 2013-08-14 | 百度在线网络技术（北京）有限公司 | Quick search method, system and device |
| CN105577651A (en) * | 2015-12-16 | 2016-05-11 | 广州酷狗计算机科技有限公司 | Service providing method and apparatus |
| CN106230831A (en) * | 2016-05-31 | 2016-12-14 | 众安在线财产保险股份有限公司 | A kind of method and system identifying browser uniqueness and feature of risk |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107508832A (en) * | 2017-09-21 | 2017-12-22 | 深圳智盾信息技术有限公司 | A kind of device-fingerprint recognition methods and system |
| CN108171074B (en) * | 2017-12-07 | 2021-03-26 | 东南大学 | Web tracking automatic detection method based on content association |
| CN108171074A (en) * | 2017-12-07 | 2018-06-15 | 东南大学 | One kind is based on the associated Web trackings automatic testing method of content |
| CN109104456A (en) * | 2018-06-07 | 2018-12-28 | 北京本邦科技股份有限公司 | A kind of user tracking based on browser fingerprint and propagating statistics analysis method |
| CN109587133A (en) * | 2018-11-30 | 2019-04-05 | 武汉烽火众智智慧之星科技有限公司 | A kind of single-node login system and method |
| CN109587133B (en) * | 2018-11-30 | 2021-07-23 | 武汉烽火众智智慧之星科技有限公司 | Single sign-on system and method |
| CN109635225A (en) * | 2018-12-14 | 2019-04-16 | 平安城市建设科技(深圳)有限公司 | Track method, apparatus, server and the storage medium of browser information |
| CN109688210A (en) * | 2018-12-14 | 2019-04-26 | 平安城市建设科技(深圳)有限公司 | Track method, apparatus, server and the storage medium of user information |
| CN109995576A (en) * | 2019-02-13 | 2019-07-09 | 平安科技（深圳）有限公司 | Recognition methods, device and the storage medium of equipment for surfing the net, computer equipment |
| CN110505231A (en) * | 2019-08-27 | 2019-11-26 | 北京丁牛科技有限公司 | One kind going anonymization tracing system and method |
| CN110866286A (en) * | 2019-10-29 | 2020-03-06 | 武汉极意网络科技有限公司 | Equipment fingerprint generation method and device |
| CN111698082A (en) * | 2020-05-29 | 2020-09-22 | 成都新希望金融信息有限公司 | Method for generating fingerprint identification of hybrid terminal equipment based on JS |
| CN111698082B (en) * | 2020-05-29 | 2023-08-25 | 成都新希望金融信息有限公司 | Method for generating fingerprint identification of hybrid terminal equipment based on JS |
| CN113067802A (en) * | 2021-03-12 | 2021-07-02 | 京东数字科技控股股份有限公司 | User identification method, device, equipment and computer readable storage medium |
| CN113067802B (en) * | 2021-03-12 | 2023-06-02 | 京东科技控股股份有限公司 | User identification method, device, equipment and computer readable storage medium |
| CN114943024A (en) * | 2022-05-31 | 2022-08-26 | 北京永信至诚科技股份有限公司 | Fingerprint acquisition method and device based on browser |
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Application publication date: 20170510 |