Methods for secure data transmission from a device

Abstract

The invention relates to a method for secure data transmission from a device based on multiple data characterizations and multiple encryption keys, wherein a subset of available data is selected by each data characterization and an encryption key is assigned to each data characterization, comprising: acquiring data, forming one or more disjoint data sets, wherein at least one selection data characterization is assigned to each data set by which each data element of the data set is selected, and for each data set: generating a falsification data set and a falsification key, applying a falsification mapping which maps the data set and the falsification data set to a falsified data set, encrypting the falsification data set with the falsification key, for each encryption key,which is assigned to at least one selection data characterization assigned to the data set, encrypting the falsification key with the respective encryption key, and transmitting the falsified data set, the encrypted falsification data set and the at least one encrypted falsification key to one or more recipients.

Description

[0001] The present invention relates to a method for secure data transmission from a device, as well as a computing unit and a computer program for carrying it out. Background of the invention

[0002] Vehicles and other devices can process image data and other data captured by sensors. In vehicles, this is particularly relevant in automated driving and driver assistance systems (AD / DA systems, AD: "assisted driving," DA: "driver assistance"). The resulting or captured data may include parts, such as specific image sections, that require protection, meaning they must not be disclosed to unauthorized parties. Examples include personal data, which may be subject to legal regulations, such as vehicle license plates or faces in camera recordings, or data used as the basis for developing AD / DA systems. Relevant data can be transferred from vehicles, such as test vehicles with additional measurement technology, to various parties, e.g.,Developers of driving functions and / or AD / DA systems receive data, for example via mobile networks, to database infrastructures such as cloud or server. Disclosure of the invention

[0003] According to the invention, a method for secure data transmission from a device, as well as a computing unit and a computer program for its execution, are proposed, comprising the features of the independent claims. Advantageous embodiments are the subject of the dependent claims and the following description.

[0004] The invention employs the measure of forming one or more data sets from captured data by means of a computing unit of the device, based on several data characterizations and the encryption keys assigned to them, wherein each data set is assigned at least one selection data characterization and wherein each data element of a data set is selected by each of the at least one selection data characterization assigned to the respective data set and is not selected by other data characterizations that differ from the at least one selection data characterization assigned to the respective data set.For each data set, a falsification data set and a falsification key are generated; a falsification mapping is applied which maps the data set and the falsification data set to a falsified data set; the falsification data set is encrypted with the falsification key; for each encryption key that is assigned to the at least one selection data characterization associated with the data set, the falsification key is encrypted with the respective encryption key in order to obtain at least one encrypted falsification key; and the falsified data set, the encrypted falsification data set, and the at least one encrypted falsification key are transmitted to one or more recipients.

[0005] The proposed approach allows for the secure, i.e., falsified, transmission of both data segments falling under single data characterizations and data segments falling under multiple data characterizations. This ensures that any recipient possessing a decryption key corresponding to the encryption key of a specific data characterization can access the segments falling under that specific data characterization. Transmitting the same falsified data sets to multiple recipients with different decryption keys from the same data set is unnecessary, thus minimizing the volume of data to be transmitted.

[0006] In particular, the distortion mapping is reversible if the distortion data set is known. The distortion mapping can be chosen such that, when using a suitable distortion data set, the data of a data set is distorted in such a way that the data in the distorted data set is no longer recognizable; that is, without knowledge of the distortion data set, the original information of the data set cannot be extracted. The distortion data sets are, for example, random strings or indistinguishable from random strings, where the term "character" is understood to mean a bit, a number, or another character (byte, word, letter, etc.).

[0007] At least one selected data characterization includes one or more of the multiple data characterizations.

[0008] In particular, the encryption keys for different data characteristics differ. The encryption of the falsified data set or its generation information is carried out using a symmetric cryptographic encryption method (e.g., AES: Advanced Encryption Standard) or an asymmetric cryptographic encryption method (e.g., RSA: Rivest-Shamir-Adleman).

[0009] According to one embodiment, at least two of the multiple data characterizations select subsets from the available data that are not disjoint. A single data element can therefore fall under two (or more) different data characterizations.

[0010] According to one embodiment, for at least one data set, the at least one selection data characterization assigned to it includes two or more data characterizations. The data elements of such a data set thus fall under multiple data characterizations, i.e., are selected by multiple data characterizations, and can be considered an intersection.

[0011] According to one embodiment, for at least one data set, the at least one selection data characterization assigned to it constitutes a single data characterization. The data elements of such a data set thus fall under exactly one data characterization and can be viewed as a difference set.

[0012] According to one embodiment, the distortion mapping is a bitwise exclusive-OR operation of the respective distortion data set with the respective data set. Bitwise exclusive-OR operations (XOR operation; i.e., as usual, a binary operation of two bits such that the result is "1" if the two bits are different and "0" if the two bits are the same) can be performed quickly and efficiently; for example, XOR operations in processors are typically implemented as machine instructions at the register level. Accordingly, the distortion data set is generated such that it has the same number of bits as the data set for which it is generated, and that there is a mapping of the bits to each other (between the bits of the data set and the bits of the corresponding distortion data set). The XOR operation is its own inverse.

[0013] According to one implementation, the falsified data sets are generated as random strings and / or random bit sequences, and / or as pseudorandom strings (or pseudorandom number sequences) and / or pseudorandom bit sequences. The falsified data sets generated in this way can be considered noise (e.g., image noise if the captured data consists of images taken with a camera) that is superimposed on the data of the transmitted data records, thus falsifying and rendering them unrecognizable. For pseudorandom strings or pseudorandom bit sequences, starting values ​​can be chosen randomly and / or based on information stored in the device that is not precisely known outside the device, e.g., the time at which the generation of the pseudorandom number sequence begins.

[0014] According to one implementation, data elements of the collected data that are not selected by any of the data characterizations are transmitted in their original form as an unaltered data set to one or more recipients. Parts of the data that do not fall under any of the data characterizations and, for example, are not intended to be protected, can thus be transmitted easily and without additional computational effort.

[0015] According to one implementation, the data to be falsified is encrypted using a symmetric cryptographic encryption method, and / or the falsification key is encrypted using an asymmetric cryptographic encryption method. Suitable, well-known encryption methods can be used, such as AES as a symmetric encryption method or RSA as an asymmetric encryption method. Using an asymmetric encryption method to encrypt the falsification keys is advantageous because the encryption keys do not need to be kept secret. Alternatively, the falsification keys can also be encrypted using a symmetric cryptographic encryption method, in which case the falsification keys should be kept secret and, in particular, should not be readable from the device's processing unit.

[0016] According to one embodiment, the method further includes transmitting, via at least one external computing unit, the multiple data characterizations and the multiple encryption keys to the device or to the device's computing unit. Alternatively, these can already be stored in the device's computing unit.

[0017] According to one embodiment, the method further comprises, by a respective computing unit of a receiver that knows a corresponding decryption key or keys for some or all of the multiple encryption keys: receiving one or more falsified data sets and, for each of the received falsified data sets, the corresponding encrypted falsification data set and the corresponding at least one encrypted falsification key; and, for each of the received one or more falsified data sets, if the receiver knows the decryption key for one of the at least one encrypted falsification keys corresponding to the respective falsified data set, decrypting the encrypted falsification key for which the receiver knows the decryption key in order to determine the falsification key;which corresponds to the respective corrupted data set, with the specified corruption key to determine the corrupted data set, and applying the inverse of the corruption mapping to the respective corrupted data set using the specified corruption data set to determine the data set. This allows the receiver to determine, in uncorrupted form, the data set for which the receiver knows the decryption key for the encryption key that is assigned to one of the at least one selection data characterization of the data set, regardless of whether or not the data set falls under further selection data characterizations (for which the receiver does not know the corresponding decryption key).

[0018] A computing unit according to the invention, e.g. a control unit of a motor vehicle, is, in particular in terms of programming, equipped to carry out a method according to the invention.

[0019] Implementing a method according to the invention in the form of a computer program or computer program product with program code for carrying out all method steps is also advantageous, as this incurs particularly low costs, especially if an executing control unit is already available for other tasks. Finally, a machine-readable storage medium is provided with a computer program stored on it as described above. Suitable storage media or data carriers for providing the computer program are, in particular, magnetic, optical, and electrical storage media, such as hard drives, flash memory, EEPROMs, DVDs, etc. Downloading a program via computer networks (Internet, intranet, etc.) is also possible. Such a download can be wired or wireless (e.g., via a WLAN network, a 3G, 4G, 5G, or 6G connection, etc.).

[0020] Further advantages and embodiments of the invention will become apparent from the description and the accompanying drawing.

[0021] The invention is schematically illustrated in the drawing using exemplary embodiments and is described below with reference to the drawing. Brief description of the drawings Fig. Figure 1 shows a flowchart of the method for secure data transmission from a device according to embodiments of the invention. Fig. 2A, Fig. Figure 2B illustrates an example of a procedure for the secure transmission of data from a vehicle. embodiment(s) of the invention

[0022] Fig. Figure 1 shows a flowchart of the method for secure data transmission from a device according to embodiments of the invention.

[0023] In an optional step 100, several data characterizations and several encryption keys are transmitted from an external computing unit to a computing unit of the device. Each data characterization selects a subset of the available data. Each data characterization is associated with one of the encryption keys, with different encryption keys being associated with different data characterizations; thus, there is a one-to-one relationship between data characterizations and encryption keys. The subsets selected by the data characterizations are, in particular, non-trivial in the sense that each data characterization is defined in such a way that it does not automatically determine the exact subsets for all different (e.g.,The empty set or the total set of the respective available data is always selected as a subset of the data available at different times (although this may be the case for certain available data in individual cases).

[0024] The encryption keys can be cryptographic keys used in either a symmetric or an asymmetric cryptographic method. In the latter case, the encryption keys are, for example, public keys of respective key pairs consisting of a public key (used to encrypt data) and a private key (used to decrypt the encrypted data). Using an asymmetric method is advantageous because the encryption key does not need to be kept secret.

[0025] A pair consisting of a data characterization and its associated encryption key, or a set of several such pairs, can be considered a capture job. Generally, one or more such capture jobs can be transmitted to the device.

[0026] It is also possible that one or more data characterizations and their associated encryption keys are already present or stored in the device, i.e., in the device's processing unit. In this case, step 100 can be omitted.

[0027] The following steps 110 to 170 are performed by the device's computing unit.

[0028] In step 110, data is acquired; that is, data present in the device is collected, such as sensor data and / or camera data and / or data from a device included in the device (e.g., a motor) and / or position data (if, for example, a GPS sensor or similar is provided in the device). Since the device can generally contain a very large amount of data, for example, in various categories (sensor, camera, etc.), it may be specified, for example, as part of an acquisition order, which data, and of which category, should be acquired. However, this can also be part of the data characterization.

[0029] In step 120, one or more datasets are created from the collected data. These datasets are disjoint, meaning that different datasets do not share any common data elements. The data elements of each dataset are selected precisely by one or more specific data characterizations, referred to as the dataset's assigned selection data characterization(s). The term "precisely" means that all data elements of a dataset are selected by each of the one or more (or at least one) selection data characterizations assigned to the dataset and are not selected by other data characterizations that are not assigned to the dataset.It therefore holds that each data set is assigned at least one selection data characterization, whereby each data element of a data set is selected by each of the at least one selection data characterization assigned to the respective data set and is not selected by other data characterizations that are different from the at least one selection data characterization assigned to the respective data set.

[0030] The following steps 130 to 170 are performed for each of the data sets created in step 120.

[0031] In step 130, a falsification data set and a falsification key are generated for the respective data set. The falsification data set can be generated, for example, as a random or pseudorandom string. The falsification key can also be generated, for example, as a random or pseudorandom string, or it can be obtained from a key stored in the processing unit using a key derivation method.

[0032] In step 140, a distortion mapping is applied, which maps the data set and the distortion data set to a distorted data set. The data set and the distortion data set can be viewed, for example, as two variables of the distortion mapping. Given the distortion data set, the distortion mapping can be considered a variable-dependent mapping applied to the data set; that is, the distortion mapping is a mapping dependent on the distortion data set, applied to the data set to determine the distorted data set. The distortion mapping is reversible given the known distortion data set. The distortion mapping is, for example, a bitwise XOR mapping (XOR: Exclusive Or), meaning that the distortion data set is generated in step 130 such that it has a corresponding bit for each bit of the data set.

[0033] In step 150, the falsified data set is encrypted with the falsification key using a symmetric cryptographic encryption method. This results in an encrypted falsified data set.

[0034] In step 160, for each encryption key associated with the at least one selection data characterization linked to the dataset, the falsification key is encrypted with the respective encryption key. Thus, for each of the at least one selection data characterization linked to the respective dataset, the encryption key associated with that selection data characterization is used to encrypt the falsification key. This means that an encryption procedure is applied one or more times (depending on the number of at least one selection data characterization) to encrypt the falsification key, using a different encryption key in each iteration. Accordingly, each of these encryption keys yields an encrypted falsification key, or at least one encrypted falsification key.

[0035] In step 170, the falsified data set, the encrypted falsification data set and at least one encrypted falsification key are transmitted (for the respective data set) to one or more recipients.

[0036] Steps 110 to 170 can be repeated for each new piece of data.

[0037] The following steps are carried out on the recipient's side, e.g. by a computing unit of the respective recipient.

[0038] The receiver knows a corresponding decryption key(s) for some or all of the multiple encryption keys, i.e., decryption keys with which data encrypted with the corresponding encryption key can be decrypted. The receiver receives one or more falsified data sets, as well as, for each of the received falsified data sets, the corresponding encrypted falsification data set and at least one corresponding encrypted falsification key. This means the data that was determined device-side for each of the data sets in steps 130 to 170.

[0039] The following steps 180 to 200 are performed for each of the received one or more corrupted data sets if the recipient knows the decryption key for at least one of the encrypted corruption keys corresponding to the respective corrupted data set. If the recipient does not know such a decryption key for a corrupted data set, the recipient can only use that corrupted data set in its corrupted form.

[0040] In step 180, the encrypted forgery key is decrypted, for which the recipient knows the decryption key in order to determine the forgery key.

[0041] In step 190, the encrypted falsification data set, corresponding to the respective falsified data set, is decrypted using the specified falsification key in order to determine the falsification data set.

[0042] In step 200, the inverse of the distortion mapping is applied to the respective distorted data set using the determined distortion data set to determine the data set.

[0043] Fig. 2A, Fig. 2B illustrates an example of a procedure for the secure transmission of data from a vehicle 2, as an example of a device.

[0044] The vehicle 2 has a camera 4 that records images 8 and / or image sequences of the vehicle's surroundings, and a processing unit 6, e.g., a control unit of the vehicle, which, with reference to Fig. 1 performs steps 110 to 170.

[0045] A first client 10 and a second client 12 transmit, e.g., via external, client-side computing units over a mobile network connection or similar, a data characterization and an associated encryption key to the vehicle 2 or its computing unit 6. The first client 10 transmits a first data characterization 22a and an associated first encryption key 24a, and the second client 10 transmits a second data characterization 22b and an associated second encryption key 24b.

[0046] The encryption keys 24a and 24b, which are assumed to be distinct from each other, enable the encryption of data using a suitable cryptographic encryption method. Furthermore, the first client 10 knows a first decryption key 26a, and the second client 12 knows a second decryption key 26b. Data encrypted with the first encryption key 24a can be decrypted with the first decryption key 26a, and data encrypted with the second encryption key 24b can be decrypted with the second decryption key 26b. The encryption method can be a symmetric or an asymmetric encryption method.In the case of a symmetric encryption method, the decryption keys 26a, 26b are equal to the corresponding encryption keys 24a, 24b. In the case of an asymmetric encryption method, each of the decryption keys 26a, 26b forms a key pair with the corresponding encryption key 24a, 24b; for example, the encryption key is a public key of the respective key pair, and the corresponding decryption key is a private key of the respective key pair.

[0047] Data characterizations 22a and 22b make it possible to select subsets of data present or recorded in vehicle 2 or its computing unit 6, which can be determined by the respective data characterization. The recorded data in the example are... Fig. 2A, Fig. 2B the images captured by the camera 8, where pixels (picture elements) are data elements of the data (conceived as a set). The data characterizations 22a, 22b give in the example of the Fig. 2A, Fig. Data characterization 2B specifies that image areas containing certain objects should be selected. An image recognition algorithm can be used to identify the corresponding image areas in captured images. For example, the first data characterization 22a specifies that image areas containing a white vehicle (regardless of whether it is a truck or a car) should be selected, and the second data characterization 22b specifies that image areas containing a car (regardless of whether it is white or not) should be selected. It should be noted that, for illustrative purposes, the following only distinguishes between two colors (white, black, shown as hatched areas in the figures) and two vehicle types (car, truck).

[0048] In the depicted image 8, for example, a white truck, a white car, and a black car are included. The first data characterization 22a thus defines, as the first subset of the data, a first image area or areas in which the white truck and the white car are contained. Similarly, the second data characterization 22b defines, as the second subset of the data, a second image area or areas in which the white car and the black car are contained.

[0049] From the first and second subsets, an intersection 30 can be formed, comprising those pixels that are contained in both subsets or image regions; a first difference set 32a, comprising those pixels that are contained in the first subset or image region (but not in the second subset or image region); and a second difference set 32b, comprising those pixels that are contained in the second subset or image region (but not in the first subset or image region). In the Fig. 2A, Fig. The example shown in 2B shows the intersection 30, i.e., white cars, the first difference set 32a, white vehicles that are not cars, and the second difference set 32b, cars that are not white.

[0050] In general, in the case of more than two data characterizations, various intersections can be formed, each containing data elements selected by at least two of these characterizations. The set of these at least two characterizations differs for different intersections, and each data element of a single intersection is selected by each of the respective at least two characterizations and is not selected by any other characterizations. Thus, each intersection is associated with at least two characterizations, and each data element of an intersection is selected from the collected data by each of the at least two characterizations associated with the intersection and is not selected by any other characterizations.For example, with three data characterizations, namely a first, a second, and a third, the following intersections can occur: an intersection where all data elements included in the intersection are selected by each of the three data characterizations; an intersection where all data elements included in the intersection are selected by the first and second data characterizations, but not selected by the third data characterization; an intersection where all data elements included in the intersection are selected by the first and third data characterizations, but not selected by the second data characterization; an intersection where all data elements included in the intersection are selected by the second and third data characterizations, but not selected by the first data characterization.Furthermore, three sets of differences can occur, whose elements are selected by exactly one of the three data characterizations.

[0051] For intersection 30 (or, in the case of multiple intersections, for each intersection) and for each of the two difference sets 32a and 32b (or, in the case of a different number of difference sets, for each difference set), a falsification data set and a falsification key are generated. Specifically, these are a falsification data set 34 and a falsification key 38 for intersection 30, a falsification data set 36a and a falsification key 40a for the first difference set 32a, and a falsification data set 36b and a falsification key 40b for the second difference set 32b. It should be noted that reference symbols are used to distinguish the falsification data sets and falsification keys, especially since they are used in the same way throughout.

[0052] The falsification data sets are used as parameters in a falsification mapping 70, which maps the intersection 30 and the two difference sets 32a and 32b to the respective falsified data sets 44, 46a, and 46b. Specifically, falsification data set 34 is used in falsification mapping 70 for the intersection 30 to obtain a falsified data set 44, falsification data set 36a is used in falsification mapping 70 for the difference set 32a to obtain a falsified data set 46a, and falsification data set 36b is used in falsification mapping 70 for the difference set 32b to obtain a falsified data set 46b.The falsification mapping 70 is in particular a bitwise XOR operation, so that the falsification data sets should be generated such that they have the same number of bits as the respective data sets, with each bit of a falsification data set corresponding to one bit of the data set for which it was generated.

[0053] The falsified data sets 34, 36a, and 36b are encrypted using the corresponding falsification keys 38, 40a, and 40b. A symmetric cryptographic encryption method is used. This process yields an encrypted falsified data set 48 by encrypting falsified data set 34 using falsification key 38, an encrypted falsified data set 50a by encrypting falsified data set 36a using falsification key 40a, and an encrypted falsified data set 50b by encrypting falsified data set 36b using falsification key 40b.

[0054] For each of the data sets, i.e., both the intersections and the differences, a falsification data set and a falsification key are generated. The falsification data set is used in the falsification mapping to determine a falsified data set from the respective data set, and the falsified data set is encrypted with the falsification key. Since this procedure is the same for each of the intersections and differences, and in particular (as described below) it is indistinguishable from the recipient's perspective whether a received falsified data set was derived from an intersection or a difference, the general term "data set" is used in the subsequent steps of the procedure (with reference symbols serving to distinguish between them).

[0055] For every dataset, at least one data characterization is given, which selects data elements of the dataset; that is, the data elements are included in the subset of the recorded data that is determined by the at least one data characterization. This at least one data characterization is also referred to as the at least one selection data characterization assigned to the dataset. Data elements of the dataset are not selected by other data characterizations that differ from the at least one selection data characterization. In the case of a difference set, the at least one selection data characterization includes exactly one data characterization; in the case of an intersection set, the at least one selection data characterization includes at least two data characterizations.

[0056] Encrypted forgery keys are further determined by encrypting the forgery keys. For each data set, the forgery key generated for that data set is encrypted with each encryption key associated with a data characterization used to select the data elements contained in that data set (independently of each other, not sequentially; the forgery key is thus encrypted with each encryption key to obtain one of the encrypted forgery keys). One or more encrypted forgery keys are obtained for each data set, namely, for each data characterization used to select the data elements contained in that data set, one encrypted forgery key is obtained, which has been encrypted with the encryption key associated with that data characterization.

[0057] In the example shown, the Fig. 2A, Fig. For the intersection of sets 2B, two encrypted falsification keys are determined, and one encrypted falsification key is determined for each of the two difference sets. Specifically, for the intersection, an encrypted falsification key 52a is determined by encrypting falsification key 38 with the first encryption key 24a, and an encrypted falsification key 52b is determined by encrypting falsification key 38 with the second encryption key 24b. For the first difference set, an encrypted falsification key 54a is determined by encrypting falsification key 40a with the first encryption key 24a.

[0058] For the second difference set, an encrypted falsification key 54b is determined by encrypting the falsification key 40b with the second encryption key 24b.

[0059] The falsified data sets 44, 46a, 46b, the encrypted falsification data sets 48, 50a, 50b and the encrypted falsification keys 52a, 52b, 54a, 54b are transmitted to one or more recipients, who in the example shown are the first and second clients 10, 12. In the example shown, where image data is used as captured data whose data elements (pixels or picture points) are arranged in a predetermined data structure (two-dimensional grid or array), the falsified data sets 44, 46a, 46b can be combined into a falsified total data set 58 and the encrypted falsified data sets 48, 50a, 50b can be combined into an encrypted total falsified data set 60 (each according to the arrangement of the data structure) and the falsified total data set 58 and the encrypted total falsified data set 60 are transmitted.

[0060] The transmission can take place, for example, via a database or database server 14, in which the falsified data sets, the encrypted falsification data sets, and the encrypted falsification keys can be temporarily stored so that they can be retrieved and transmitted to the clients and / or other recipients. In the example shown for a vehicle 2, the transmission to the database server 14 and / or the recipients can take place, for example, via a radio or mobile communication connection.

[0061] Each recipient, e.g. by means of a receiver-side computing unit, decrypts the encrypted falsification keys 52a, 52b, 54a, 54b insofar as the respective recipient has access to the decryption key that corresponds to the encryption key with which the respective encrypted falsification key was encrypted (if the transmitted data does not contain any information that can be used to identify which encrypted falsification key is to be encrypted with which decryption key, a single recipient can indeed attempt to perform the decryptions for all encrypted falsification keys with all decryption keys available to that recipient, but will then only be able to reconstruct meaningful data sets for which the decryption key was actually suitable for decrypting the respective encrypted falsification key).

[0062] In the example shown, the first client 10, who knows the first decryption key 26a, decrypts the encrypted falsification key 52a with the first decryption key 26a to obtain the falsification key 38, and the encrypted falsification key 54a with the first decryption key 26a to obtain the falsification key 40a. The second client 12, who knows the second decryption key 26b, decrypts the encrypted falsification key 52b with the second decryption key 26b to obtain the falsification key 38, and the encrypted falsification key 54b with the second decryption key 26b to obtain the falsification key 40b.

[0063] The recipients use the falsification keys 38, 40a, and 40b, insofar as they were able to obtain them, to decrypt the encrypted falsification data sets. The first client, 10, decrypts the encrypted falsification data set 50a with the falsification key 40a to determine the falsification data set 36a, and the encrypted falsification data set 48 with the falsification key 38 to determine the falsification data set 34. The second client, 12, decrypts the encrypted falsification data set 50b with the falsification key 40b to determine the falsification data set 36b, and the encrypted falsification data set 48 with the falsification key 38 to determine the falsification data set 34.The clients can therefore, on the one hand, both determine the falsification data set 34 assigned to the intersection, and on the other hand, only determine that of the falsification data sets 40a, 40b which is assigned to the difference set of the respective client.

[0064] In Fig. In Figure 2B, these decryptions are represented as decryptions of the encrypted total falsification data set 60. However, since the respective falsification key is only the correct falsification key for the corresponding falsification data set (which is part of the total falsification data set), this can be seen as determining the respective (unencrypted) falsification data set. Such a procedure is conceivable for image data or, more generally, for captured data arranged in a data structure.

[0065] The recipients can reconstruct, using the respective specified falsification data sets used in an inversion 72 of the falsification mapping, those data sets containing data elements selected by the respective client's data characterization 22a, 22b. The inversion 72 of the falsification mapping is applied to the respective falsified data sets, whereby, if falsification mapping 70 is an XOR operation with the respective falsification data set, its inversion 72 is also an XOR operation with the respective falsification data set.In the example shown, where the captured data are images whose pixels are arranged in a predetermined two-dimensional array (as a predetermined data structure), the total distorted data set 58 can be used instead of the individual distorted data sets (whereby a clear image is only recognizable in the image areas where the distorted data matches the distorted data, and only image noise is visible in other image areas).

[0066] In this way, the first client 10 can receive the first difference set 32a and the intersection set 30, and the second client 12 can receive the second difference set 32b and the intersection set 30. Overall, the first client 10 receives images 74a in which all image areas falling under the first data characterization 22a are clearly identifiable, and image areas falling exclusively under the second data characterization 22b (for which the first client does not know the corresponding decryption key 26b) are not identifiable. The second client 12 receives images 74a in which all image areas falling under the first data characterization 22a are clearly identifiable, and image areas falling exclusively under the second data characterization 22b (for which the first client does not know the corresponding decryption key 26b) are not identifiable.

Claims

[1] Method for secure data transmission from a device based on several data characterizations (22a, 22b) and several encryption keys (24a, 24b), wherein each data characterization (22a, 22b) selects a subset of available data and each data characterization (22a, 22b) is assigned an encryption key (24a, 24b), wherein different data characterizations (22a, 22b) are assigned different encryption keys (24a, 24b); the method comprising a computing unit (6) of the device (2): Collecting (110) data; Form (120) one or more disjoint data sets (30, 32a, 32b) from the recorded data, wherein each data set is assigned at least one selection data characterization and wherein each data element of a data set is selected by each of the at least one selection data characterization assigned to the respective data set and is not selected by other data characterizations that are different from the at least one selection data characterization assigned to the respective data set; for each data set (30, 32a, 32b): - Generating (130) a forgery data set (34, 36a, 36b) and a forgery key (38, 40a, 40b); - Applying (140) a distortion mapping (70) which maps the data set (30, 32a, 32b) and the distortion data set (34, 36a, 36b) to a distorted data set (44, 46a, 46b); - Encrypting (150) the forgery data set (34, 36a, 36b) with the forgery key (38, 40a, 40b) to obtain an encrypted forgery data set (48, 50a, 50b); - for each encryption key (24a, 24b) that is assigned to the at least one selection data characterization associated with the data set, encrypt (160) the falsification key (38, 40a, 40b) with the respective encryption key (24a, 24b) in order to obtain at least one encrypted falsification key (52a, 52b, 54a, 54b); and - Transmitting (170) the falsified data set (44, 46a, 46b), the encrypted falsification data set (48, 50a, 50b) and at least one encrypted falsification key (52a, 52b, 54a, 54b) to one or more recipients (10, 12). [2] Method according to claim 1, wherein at least two of the multiple data characterizations (22a, 22b) select subsets from available data that are not disjoint. [3] Method according to one of the preceding claims, wherein for at least one data set (30) the at least one first selection data characterization assigned to it includes two or more data characterizations (22a, 22b). [4] Method according to one of the preceding claims, wherein for at least one data set (32a, 32b) the at least one selection data characterization assigned to it is a single data characterization. [5] Method according to one of the preceding claims, wherein the falsification image (70) is reversible when the amount of falsification data (34, 36a, 36b) is known. [6] Method according to any of the preceding claims, wherein the falsification mapping (70) is a bitwise exclusive-OR operation of the respective falsification data set (34, 36a, 36b) with the respective data set (30, 32a, 32b). [7] Method according to any of the preceding claims, wherein the falsification data sets (34, 36a, 36b) are generated as random strings and / or random bit sequences; and / or wherein the falsification data sets (34, 36a, 36b) are generated as pseudorandom strings and / or pseudorandom bit sequences. [8] Method according to one of the preceding claims, wherein data elements of the recorded data which are not selected by any of the data characterizations (22a, 22b) are transmitted in unchanged form as an unaltered data set to the one or more recipients (10, 12). [9] Method according to any of the preceding claims, wherein the encryption of the forgery data set (34, 36a, 36b) is carried out using a symmetric cryptographic encryption method; and / or wherein the encryption of the forgery key (38, 40a, 40b) is carried out using an asymmetric cryptographic encryption method. [10] Method according to any one of the preceding claims, further comprising: Transmit (100) the multiple data characterizations (22a, 22b) and the multiple encryption keys (24a, 24b) to the device (2) or to the computing unit (6) of the device (2) by at least one external computing unit. [11] Method according to one of the preceding claims, further comprising a respective computing unit of a receiver (10, 12) which knows a corresponding decryption key or decryption keys (26a, 26b) for some or all of the multiple encryption keys (24a, 24b): Receiving one or more corrupted data sets and for each of the corrupted received data sets (44, 46a, 46b) the corresponding encrypted corrupted data set (48, 50a, 50b) and the corresponding at least one encrypted corrupted key (38, 40a, 40b); for each of the received one or more corrupted data sets (44, 46a, 46b), if the recipient knows the decryption key for one of the at least one encrypted corruption keys corresponding to the respective corrupted data set: - Decrypting (180) the encrypted falsification key (52a, 52b, 54a, 54b) for which the recipient knows the decryption key (26a, 26b) in order to determine the falsification key (38, 40a, 40b); - Decrypting (109) the encrypted falsification data set (48, 50a, 50b) corresponding to the respective falsified data set (44, 46a, 46b) with the determined falsification key (38, 40a, 40b) to determine the falsification data set (34, 36a, 36b); - Applying (200) the inverse (72) of the falsification mapping to the respective falsified data set (44, 46a, 46b) using the determined falsification data set (34, 36a, 36b) to determine the data set (30, 32a, 32b). [12] Computing unit (6) configured to perform all process steps of a method according to any one of claims 1 to 11. [13] Computer program that causes a computing unit (6) to perform all the process steps of a method according to any one of claims 1 to 9 when executed on the computing unit. [14] Machine-readable storage medium with a computer program stored thereon according to claim 13.

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