RU2748966C1 - Method for synchronisation of anonymous identifiers - Google Patents

Abstract

FIELD: information technology.SUBSTANCE: invention relates to the area of computing technology for protecting confidentiality of user data and ensuring privacy. The technical result is achieved through obtaining identifiers of one user in at least two data management systems; generating an anonymous identifier for each received user identifier by a transformation tool; generating a random token for each user identifier-anonymous identifier cluster by the transformation tool; sending the received tokens from the source to the receiver; displaying on demand each random token to the anonymous identifier by the transformation tool; combining the anonymous identifiers on the receiver corresponding with the tokens received from the same source.EFFECT: technical result of the present invention is provided integration of anonymous identifiers.10 cl, 9 dwg

Description

Technology area

The invention relates to anonymization methods and systems for protecting the confidentiality of user data and ensuring privacy.

State of the art

An individual approach has always been the best way to convey information to the consumer. This approach has long been a decisive factor in the most successful marketing campaigns, and direct marketing gained widespread popularity at the end of the 20th century, because there were more and more opportunities for an individual approach to the client. The growth of digital technologies provides more and more new tools for this. At the moment, many surveys of marketers in recent months show that improving personalization is the top priority for the coming years.

Behavioral personalization identifies human behavior through signals received through various data collection points, such as visits to a website, e-mails, searches for and consumption of certain content, or even visits to certain parts of the store. All of this information can be incorporated into a predictive model that enables real-time decision making. But by developing these mechanisms, ad networks are constantly faced with a privacy issue. The amount of information about customers that companies collect is simply immense, and many questions arise in connection with the storage of the collected personal information and the guarantee of anonymity.

To collect user data, various analytical utilities and trackers (from the English tracker) can be used, which are html and js code embedded on a web resource page. Each tracker has its own domain, therefore, the user identifier within one tracker is an arbitrary identifier stored in a cookie (from the English cookie) on the client. Depending on the functionality of the tracker, data of varying completeness may be collected. For example, for a user registered via e-mail, you can bind a postal address to the identifier, and in the case of search queries, collect a set of user interests. To increase the completeness of the user profile, data from various trackers are collected in a common database - a data storage and management system, for example, a Data Management Platform (hereinafter referred to as DMP). To associate data on different identifiers from cookies from one user, the cookie synchronization mechanism is used. In general, cookie synchronization is done both ways and can involve multiple data management systems. Data management systems expose redirects (jarg. Redirect from English. Redirect) to other systems, passing the content of their cookie-file as a parameter of the network address. This allows you to match two identifiers, one passed in the cookie and the other in the network address parameter on the system to which the redirect was made. As a result of the synchronization process, the DMP is a user profile that is collected from several sources and also contains user IDs from cookies on various systems. Systems participating in synchronization can also obtain a user ID from the DMP. Synchronization between data management systems of the Supply Side platform (hereinafter referred to as SSP) and the DMP type is especially relevant, since the SSP puts up its identifier for the auction, and the data in the Demand Side Platform (hereinafter DSP) participating in the auction is tied to the DMP identifiers.

In view of the adoption of new laws in the field of personal data (for example, the General Data Protection Regulation, GDPR), it becomes necessary to ensure confidentiality and preserve the described advertising mechanisms. It follows from the analysis of the prior art that some mechanisms are already known that allow hiding valid identifiers, for example:

- US 20122866 describes a method of storing data in cloud storage, in which any file from the user's device goes to a proxy server, where it is encrypted and transferred to the cloud storage;

- US 8739265 describes a method for obfuscating and extracting data, where data containing confidential data sent from a device is intercepted, modified and transmitted further to the cloud storage. In this case, not all data is changed, but determined in accordance with the rules set in the network, and this data may not be transmitted to the cloud, but retrieved and stored locally, and pop sensitive data and a token will be transmitted instead of the retrieved data.

Based on the analysis of the state of the art, it becomes clear that the described means require the installation of an additional agent on the user's device, which makes it difficult for the user to work with the remote server and makes the interaction opaque.

Brief Description of Drawings

The accompanying drawings are included to provide a further understanding of the invention and form part of this description, show embodiments of the invention, and together with the description serve to explain the principles of the invention.

The claimed invention is illustrated by the following drawings, in which:

FIG. 1 - depicts a system for anonymous data exchange.

FIG. 2 depicts a method for receiving data by a receiver from a source.

FIG. 3 shows an example of an embodiment of a method for receiving data by a receiver from a source.

FIG. 4 depicts a system for synchronizing anonymous identifiers.

FIG. 5 depicts a method for synchronizing anonymous identifiers.

FIG. 6 shows an example of an implementation of a method for synchronizing anonymous identifiers.

FIG. 7 - depicts a system for obtaining anonymous data.

FIG. 8 - depicts a method for obtaining anonymous data.

FIG. 9 depicts an example of a general purpose computer system.

Disclosure invention

The present invention is intended to synchronize anonymous identifiers when exchanging data in data management systems. The technical result of the present invention is to provide the unification of anonymous identifiers as a result of providing a method in which identifiers of one user are obtained in at least two data management systems. Data management systems can be SSP, DSP, DMP. Next, an anonymous identifier is generated by the transforming means for each received user identifier. Next, the conversion means generates a random token for each linking user identifier-anonymous identifier. The token is formed on the basis of data from the source, identification data can be used as data, for example: identifiers, data for creating unique fingerprints, unique fingerprints, an identifier in the platform for data management, an identifier from a synchronization tool. Based on the data from the source, an anonymous identifier is additionally formed, where the generated random token can be unambiguously, in a particular case, mapped to an anonymous identifier. Tokens are returned to the source and the received tokens are sent from the source to the receiver; by the on-demand transformator, the receiver maps each random token to an anonymous identifier and returns the anonymous identifiers to the receiver. Combine anonymous identifiers on the sink that match tokens received from the same source.

The sink and / or source can be data management platforms where the source is the sender of the data and the sink is the destination of the data. Also, the source in the client-server architecture is the client, and the sink is the server. The conversion tool is located on a network node other than the destination and / or source network. In a particular case, the generation of a token is initiated by the source.

Description of embodiments of inventions

The objects and features of the present invention, methods for achieving these objects and features will become apparent by reference to exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below, but may be embodied in various forms. The foregoing description is intended to assist a person skilled in the art for a comprehensive understanding of the invention, which is defined within the scope of the appended claims. Below is a disclosure of the key concepts used in the application.

The claimed invention makes it possible to manage the collected data and break the connection between the user and the data management systems, as well as to abandon the traditional mechanisms for setting a cookie and obtaining user consent for each installation. The user is legally assigned the right to revoke permission to process data, the present invention provides the technical implementation of this right "in one click" for an unlimited number of trackers. And the claimed invention can be built into existing RTB (real time bidding) mechanisms.

FIG. 1 depicts a system for anonymous data exchange. The system includes a data source 110, a data sink 120, and a data conversion means 130. The data source 110 contains data that is provided to the receiver 120 upon request. The data conversion tool 130 is designed to create links:

- user ID ↔ anonymous ID,

- critical data ↔ anonymous data.

Transformer 130 also generates random tokens for these bundles. The transform tool can convert identities and critical data in both directions, where the transformation results in anonymous identifiers or anonymous data. In a particular case, the methods of data transformation are:

- quantization;

- sorting;

- merging (gluing);

- grouping;

- setting up a dataset;

- table substitution of values;

- calculated values;

- data coding;

- encryption;

- normalization (scaling);

- convolution.

In this case, the transformation in a particular case is carried out without the possibility of the inverse transformation ¹ ( ¹ , the inverse transformation is understood as a transformation that allows you to restore the original form of the transformation object (data, substructure) before the transformation) by any means other than the means of anonymizing the node. In the general case, a transformation means a mapping (function) of a set into itself, or, in other words, transformations are called mappings that transfer a set to another set ² ( ² Mathematical Encyclopedic Dictionary. - M., 1988. - S. 487).

In a particular case, the conversion means 130 is located at a network node other than the receiver and / or source network. In a particular case, a node with a conversion tool is located in a regional network ³ ( ^{3 A} geographically dispersed network that unites computers at different points into a single whole by means of communication means, a set of regional networks forms a global network), different from the regional network in which the receiver ⁴ is located ( ⁴ This allows you to locate system elements in independent jurisdictions), and / or is not in the same Intranet ⁵ ( ⁵ GOST R ISO 9241-151-2014) with a receiver or source. Moreover, within the framework of this invention, different regional networks are not only geographically dispersed, but also located in different jurisdictions, therefore, obviously, within the framework of this invention, different regional networks can be, among other things, networks connecting nodes of countries (national networks). In GDPR terminology, for example, the regional network of the Russian Federation, in which the server is located, will be considered the regional network of a third country. In a particular case, the regional network of the node with the transforming means is also different from the regional network of the client.

Data sources 110 can be clients, various user devices, IoT devices, or data management systems such as a user databank that aggregates and stores all user data and which the user controls through an interface. Servers, data management systems, including CDP, DMP, SSP, DSP, etc. can act as receivers. In a particular case, the opposite situation is possible, when sources become receivers, and receivers become sources. For example, in RTB technology, when the user's device receives an ad unit from the SSP. In this example, the SSP is the source and the user device is the sink.

FIG. 1 depicts an example of an interaction model between a data source 110 and a data sink 120, where:

- the receiver requests data from the source (210);

- the source sends the identifier to the transforming means (220);

- the conversion tool returns a random token to the source (230);

- the source sends to the receiver the requested data, combined with the token (240);

- the receiver sends a random token to the conversion tool (250);

- the conversion means returns the anonymous identifier to the receiver (260);

FIG. 2 depicts a method 200 for receiving data by a receiver from a source that uses the described model. At step 210, source 110 receives a request from receiver 120 to send data. In a particular case, the send request may not be initiated by the receiver 120, but initiated within the source 110 itself. For example, when a script is executed on the user's device that initiates the sending of data to the web server or DMP. Having received the request, the source prepares the data for sending, for this, the user ID or any other identification data (along with which the data should be sent to the receiver 120) is not associated with the data to be sent, and if the binding has already occurred, the identification data is separated by the modifier 140 (in detail below ), and at step 220 are sent to the conversion means 130. In the particular case, if the user identifier is absent (verification is performed at step 211), for example, the cookie is not set or is missing, then this identifier is generated at step 212. The user identifier can be generated as a source 110 and a third party (for example: DMP, SSP, web server, databank, etc.), and sent to the source in step 2126. The transforming means 130 in step 221 checks whether an existing anonymous identifier is associated with the received credentials. If there is no connection, at step 222 an anonymous identifier is generated and a user identifier-anonymous identifier pair is created. In a particular case, the user's identification data is unambiguously associated with an anonymous identifier (one-to-one relationship - injection). In another special case, the relationship is ambiguous (one-to-many):

- several identifiers of the same user are associated with one anonymous identifier (for example, when one user has identifiers in several systems);

- identifiers of several users are associated with one anonymous identifier (for example, when an anonymous identifier characterizes a certain group of people - family / colleagues or an identifier characterizes a consumer segment);

- several anonymous identifiers are associated with one user identifier (for example, when it is necessary to provide a controlled collision of identifiers for depersonalization and the impossibility of reverse transformation).

At step 223, a random token is generated for the user ID-anonymous ID pair. And at step 230, this random token is returned to the source 110. The random token is combined with data intended for the receiver (step 231), and the data with the token is transmitted at step 240 to the receiver 120. The token from the receiver 120 at step 250 is transmitted to the transform 130, where the token is mapped to an anonymous identifier from a pair of user identifier-anonymous identifier for which the token was generated (block 255). The data is combined with the returned (at step 260) anonymous identifier at step 265. In the particular case, if the returned anonymous identifier was already known to the receiver, the data received from the source 110 is stored in the structure previously created for this anonymous identifier, otherwise a new structure is created storage for data associated with an anonymous identifier. Several anonymous identifiers under one user identifier are created when it is necessary to ensure the anonymity of the user. In this case, one user identifier is mapped to several anonymous identifiers, which does not allow the receiver to combine data under one identifier and de-anonymize the user.

FIG. 3 shows an embodiment of a method for sending data using the example of collecting DMP data. In step 210a, the browser receives the scripted html page from the web server 310. At least one of the scripts is designed to collect data from the user's device 150, where the browser 110a is installed, and send the data to the DMP 120a. To send data, you need to generate a unique user ID, for this, at step 212a, a DMP Middleware 320a request is sent. The DMP Middleware at step 212 generates a user identifier dmpUserID and returns it to the browser at step 2126. At step 220, the dmpUserID is sent to the conversion means 130. The conversion means 130 checks whether an existing anonymous identifier is associated with the retrieved identifier. If there is no connection, the anonymous identifier anonymUserID is generated by the transform tool 130, otherwise the existing one is used. Next, a random dmpUserIDToken is generated for the dmpUserID-anonymUserID pair. And at step 230, the generated dmpUserIDToken is returned to the user's device. The random token dmpUserIDToken is combined with DATA data intended for DMP. The DATA data with the token dmpUserIDToken at step 240 is sent to the DMP. The dmpUserIDToken from the DMP at step 250 is passed to the transformer 130, where the token is mapped to the anonymous identifier anonymUserID from the dmpUserID-anonymUserID pair for which the token was generated. And the anonymUserID is returned at 260 by DMP. DATA data on DMP 120a is combined with the returned anonymous identifier anonymUserID In the particular case, if the returned anonymous identifier was already known to the DMP, the data received from the web server is stored in the structure previously created for this anonymous identifier, otherwise a new storage structure is created for the data associated with an anonymous identifier.

FIG. 4 shows a system for synchronizing identifiers 400. The means of system 400 are similar to those of system 100. Additionally, system 400 contains data management systems 320, of which there are at least two for synchronization. FIG. 4 shows an example of a model of interaction between a data source and a data sink, where:

- the source 110 sends a request to obtain an identifier to the control system 320 (212a);

- system 320 returns the identifier to the source 110 (2126);

- the source sends the identifier to the transforming means (220);

- the conversion tool returns a random token to the source (230);

- the source sends tokens to the receiver (241);

- the receiver sends the token to the conversion tool (250);

- the transforming means returns the anonymous identifier to the receiver (260).

FIG. 5 depicts a method for sending data using the described interaction model. If the source 110 does not have identifiers to synchronize, then the source 110 sends a request 212a to obtain the identifier to the control system 320, the system 320 generates the identifier in step 212 and returns it to the source 110 (2126). The source 110 transmits the identifiers to the transform 130 in step 220. The transform means in step 221 checks whether an existing anonymous identifier is associated with the received credentials. If there is no connection, at step 222 an anonymous identifier is generated and associated with identification data, as in method 200. At step 223, a random token is generated for each pair of user identifier - anonymous identifier. And at step 230, this random token is returned to the source 110. All tokens of the source 110 are combined in step 231a and transmitted to the receiver in step 241. The tokens from the receiver in step 250 are transferred to the transform 130, where the tokens are mapped (jar. Resolve) to anonymous identifiers from the user identifier-anonymous identifier pairs, for which tokens were generated (step 255). The anonymous identifiers are returned to the receiver 120 at 260 and at 266 the anonymous identifiers are associated with the tokens received from the same source.

FIG. 6 depicts an embodiment of the invention using the example of synchronization of identifiers from SSP and from DMP. Browser 110a sends identifier requests 212a to SSP Middleware 3206 and DMP Middleware 320a. System 320a generates dmpUserID and system 3206 generates sspUserID. The generated identifiers at block 2126 are returned to the device of the user 150, whose browser 110a sent the request. The identifiers are sent to the transform 130 in step 220. The anonymous identifiers are generated by the transform 130. For dmpUserID, anonymDmpUserID is formed, and for sspUserID, anonymSspUserID is formed. A random token is generated for each pair:

- dmpUserID-anonymDmpUserID ↔ dmpUserIDToken;

- sspUserID- anonymSspUserID ↔ sspUserIDToken.

And at step 230, sspUserIDToken and dmpUserIDToken tokens are returned to device 150. The received tokens on the device are combined and transferred to the DMP 120a at step 241. The tokens from the DMP at step 250 are transferred to the transformer 130, where the tokens are mapped to anonymous identifiers. The anonymous identifiers anonymDmpUserID and anonymSspUserID return DMP in step 260, and in DMP, the identifiers are linked (synchronized) as identifying the same source in different systems 320.

FIG. 7 depicts a system for sending anonymous data 700. The means of system 700 are similar to those of system 100. System 700 includes modification means 140 for separating data structures (which are generated for sending from source 110 to receiver 120). The criteria for dividing a data structure into substructures can be different, one of these criteria is the presence in the sent data of personal data (Personal Identification Information) or their special categories (in GDPR terminology), in which the data structure is divided in such a way that one substructure contains personal data (hereinafter PD, English PII) or their special categories, another substructure includes data that is not personal. The relationship of data to personal is determined, for example, by the legislation of the country in the jurisdiction of which the user is using the device, which is the client in the described system. In other words, by the location of the data source. Another more general criterion is the availability of critical data. Critical data refers to data in respect of which the law or an authorized entity imposes restrictions on the collection, storage, access, distribution and processing. These data, as a rule, are sensitive to disclosure, distribution, leakage, since the occurrence of these events leads to a violation of the rights and legally protected interests of subjects and provides for liability for subjects who have violated the rules for collecting, storing, accessing, processing such data. A special case of critical data is confidential data ⁶ ( ⁶ or confidential information, within the framework of this application confidential data and confidential information are synonyms) (English sensitive data). Confidential data means data that is protected in accordance with the laws of the country in the jurisdiction of which the user is using the device, which is a client in the described system. In a particular case, confidential data includes personal data (PD) and data containing:

- commercial secrets;

- tax secrets;

- bank secrecy;

- medical confidentiality;

- notarial secret;

- attorney-client privilege;

- audit secrecy;

- communication secrecy;

- insurance secrecy;

- secret of the will;

- the secret of adoption;

- the secret of confession;

- the secrecy of the investigation;

- secrecy of legal proceedings;

- information about the protected persons;

- state secrets.

FIG. 7 shows an example of a model for sending anonymous data, where:

- the receiver requests data from the source (210)

- the source 110 sends a request to obtain an identifier to the control system 320 (212a)

- system 320 returns the identifier to the source 110 (2126).

- the source sends the identifier to the transforming means (220)

- the source sends critical data (220a)

- the transforming tool returns a random token for the identifier to the source (230)

- the transform tool returns a random data token to the source (230

- the source sends tokens and non-critical data to the receiver (242)

- the receiver sends tokens to the conversion facility (250)

- the transformer returns an anonymous identifier to the receiver (260)

- the transformer returns anonymous data to the receiver (261)

FIG. 8 depicts a method for sending anonymous data 200 that uses the described sending model. At step 210, source 110 receives a request from receiver 120 to send data. In a particular case, if the user identifier is absent (verification is carried out at step 211), for example, the cookie is not set, then this identifier is generated at step 212. The user identifier can be generated both by the source 110 and by a third party (for example: DMP, SSP, web server, databank, etc.) and sent to the source 110. The identifier from the source is sent at step 220 to the transform 130. The data to be sent is checked at step 213 for critical data. If critical data is detected, then at step 220a, the critical data is sent to transform 130. Steps 220 and 220a may be performed both synchronously and asynchronously.

Conversion means 130 checks in step 221 whether an existing anonymous identifier is associated with the received identity. If there is no connection, at step 222 an anonymous identifier is generated and a user identifier-anonymous identifier pair is created, similar to method 200. The transforming means at step 222a anonymizes the received critical data and creates another critical data-anonymous data pair. At block 223, random tokens are generated for the user identifier-anonymous identifier pair and the critical data-anonymous data pair.

At step 230, a random token is returned to the source 110 for the user ID-anonymous identifier pair, and at step 230a, a random token is returned for the critical data-anonymous data pair. All tokens of the source 110 and in the particular case non-critical data are transmitted to the receiver at step 241. The tokens from the receiver at step 250 are transferred to the conversion means 130, where the token from the user ID-anonymous identifier pair is mapped to the anonymous identifier (step 255), and the token from the pair critical data - anonymous data is mapped to anonymous data (block 256). The anonymous identifier is returned to the receiver 120 in step 260, and the anonymous data is returned to the receiver 120 in step 261. In step 267, the anonymous identifier, the anonymous data and, if transmitted, non-critical data are combined. In the special case, if the returned anonymous identifier was already known to the receiver, the data received from the source 110 and the anonymous data received from the token conversion means are stored in the structure previously created for this anonymous identifier, otherwise a new storage structure for the data is created. associated with an anonymous identifier. Steps 260 and 261 can be performed both synchronously and asynchronously.

System 100, 400 and 700 means real devices, systems, components, a group of components implemented using hardware, such as an application-specific integrated circuit (ASIC) or field-programmable gate array. gate array, FPGA) or, for example, in the form of a combination of software and hardware, such as a microprocessor system and a set of software instructions, as well as on neurosynaptic chips, the functionality of these modules can be implemented exclusively by hardware, as well as in the form of a combination, where part of the functionality is implemented by software, and part by hardware. In some implementations, the tools may be executed on a processor of a general purpose computer (eg, as depicted in FIG. 9). Databases can be implemented in all possible ways and contained both on one physical medium, and on different ones, located both locally and remotely.

FIG. 9 shows an example of a general-purpose computer system, a personal computer or server 20, comprising a central processing unit 21, a system memory 22, and a system bus 23 that contains various system components, including memory associated with the central processing unit 21. The system bus 23 is implemented as any bus structure known from the prior art, which in turn contains a bus memory or a bus memory controller, a peripheral bus and a local bus that is capable of interfacing with any other bus architecture. System memory contains read-only memory (ROM) 24, random access memory (RAM) 25. The main input / output system (BIOS) 26 contains basic procedures that transfer information between the elements of the personal computer 20, for example, at the time of loading the operating room. systems using ROM 24.

The personal computer 20, in turn, contains a hard disk 27 for reading and writing data, a magnetic disk drive 28 for reading and writing to removable magnetic disks 29 and an optical drive 30 for reading and writing to removable optical disks 31, such as CD-ROM, DVD -ROM and other optical media. The hard disk 27, the magnetic disk drive 28, and the optical drive 30 are connected to the system bus 23 via the hard disk interface 32, the magnetic disk interface 33, and the optical drive interface 34, respectively. Drives and corresponding computer storage media are non-volatile storage media for computer instructions, data structures, program modules and other data of a personal computer 20.

The present description discloses an implementation of a system that uses a hard disk 27, a removable magnetic disk 29 and a removable optical disk 31, but it should be understood that other types of computer storage media 56 are possible that are capable of storing data in a computer readable form (solid state drives, flash memory cards, digital disks, random access memory (RAM), etc.), which are connected to the system bus 23 through the controller 55.

Computer 20 has a file system 36, where the recorded operating system 35 is stored, as well as additional software applications 37, other program modules 38 and program data 39. The user has the ability to enter commands and information into the personal computer 20 through input devices (keyboard 40, manipulator " mouse "42). Other input devices may be used (not shown): microphone, joystick, game console, scanner, etc. Such input devices are conventionally connected to the computer system 20 through a serial port 46, which in turn is connected to the system bus, but can be connected in another way, for example, using a parallel port, a game port, or a universal serial bus (USB). A monitor 47 or other type of display device is also connected to the system bus 23 via an interface such as a video adapter 48. In addition to the monitor 47, the personal computer may be equipped with other peripheral output devices (not displayed), such as speakers, a printer, etc. ...

The personal computer 20 is capable of operating in a networked environment using a network connection with other or more remote computers 49. The remote computer (or computers) 49 are the same personal computers or servers that have most or all of the elements mentioned earlier in the description of the entity. the personal computer 20 shown in FIG. 9. In a computer network, there may also be other devices, such as routers, network stations, peer-to-peer devices or other network nodes.

Network connections can form a local area network (LAN) 50 and a wide area network (WAN). Such networks are used in corporate computer networks, internal networks of companies and, as a rule, have access to the Internet. In LAN or WAN networks, personal computer 20 is connected to local network 50 via a network adapter or network interface 51. When using networks, personal computer 20 may use a modem 54 or other means of providing communication with a wide area network, such as the Internet. Modem 54, which is an internal or external device, is connected to the system bus 23 via a serial port 46. It should be noted that the network connections are only exemplary and are not required to reflect the exact configuration of the network, i. E. in fact, there are other ways of establishing a connection by technical means of communication of one computer with another.

In conclusion, it should be noted that the information given in the description are examples, which do not limit the scope of the present invention defined by the claims. One skilled in the art will appreciate that there may be other embodiments of the present invention consistent with the spirit and scope of the present invention.

Claims (16)

1. A way of combining anonymous identifiers, in which: a) obtain identifiers of one user in at least two data management systems; b) an anonymous identifier is generated by the transformation means for each received user identifier; c) a random token is generated by the conversion means for each linking user identifier-anonymous identifier; d) send the received tokens from the source to the receiver; e) by means of converters on demand, map each random token to an anonymous identifier; f) combine anonymous identifiers at the sink that correspond to tokens received from the same source. 2. The method according to claim 1, in which a token is generated based on data from a source. 3. A method according to claim 2, in which identification data (identifiers, data for creating unique fingerprints, unique fingerprints, an identifier in the platform for data management, an identifier from a synchronization tool) are used as data. 4. The method according to claim 1, wherein an anonymous identifier is additionally generated based on the data from the source. 5. The method according to claim 4, wherein the generated random token is uniquely mapped to an anonymous identifier. 6. The method according to claim 1, wherein the receiver and / or the source are data management platforms, where the source is the sender of the data and the receiver is the receiver of the data. 7. The method according to claim 6, wherein the data management systems SSP and DMP. 8. The method of claim 1, wherein the source is the client and the sink is the server. 9. The method of claim 1, wherein the transforming means is located at a network node other than the receiver and / or source network. 10. The method according to claim 1, wherein the generation of the token is initiated by the source.

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