Multi-party joint risk prediction method and system based on anti-quantum privacy calculation

Abstract

The invention particularly relates to a multi-party joint risk prediction method and system based on anti-quantum privacy calculation. According to the multi-party joint risk prediction method based on anti-quantum privacy calculation, each participant of joint risk analysis constructs a PKI system by using a Diithium algorithm, and signs and issues a certificate; a PKI system is combined with an anti-quantum non-interactive zero-knowledge certification system, an application certificate is registered, and public keys of the certificate are mutually exchanged; in combination with the public key certificate and the local database index value of each mechanism, a PSI protocol is operated, and intersection among local database indexes of all parties is achieved; and executing a pre-calculation algorithm to generate a series of beader triples, jointly calculating a risk prediction function F in combination with an intersected index value, and returning a risk prediction index to a specified participant. According to the multi-party joint risk prediction method and system based on anti-quantum privacy calculation, the security of data of each party is guaranteed under the attack of a quantum computer, the number of feature values in a risk prediction model is enriched, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of data security technology, and in particular to a multi-party joint risk prediction method and system based on quantum privacy-resistant computing. Background Art

[0002] Data security is an indispensable cornerstone for protecting a company's core assets, maintaining customer trust, and ensuring business continuity and compliance operations.

[0003] Risk prediction algorithms are a key tool for modern enterprises to move forward steadily in a complex and changing environment. By deeply mining historical data and real-time information, risk prediction algorithms can accurately identify potential risks, quantify their probability of occurrence and potential impact, and provide management with forward-looking decision-making basis. This not only helps companies to plan ahead and avoid potential crises, but also capture opportunities in risks and promote business innovation and growth. In a highly competitive market, having an efficient and accurate risk prediction algorithm means being able to respond to market changes more quickly and optimize resource allocation. In addition, risk prediction algorithms also promote the refinement and intelligence of corporate risk management, improve overall operational efficiency and risk resistance, and are one of the indispensable core competitiveness of modern enterprises.

[0004] However, existing risk prediction algorithms face many challenges. First, a single company directly using its local data to make a risk prediction model usually faces the problem of insufficient data samples and single eigenvalues, so the accuracy of the risk prediction it produces is low. Secondly, risk prediction often involves a large amount of sensitive data, such as customer personal information, corporate transaction records, etc. When multiple parties' data are jointly calculated, there is a risk of sensitive data leakage. Finally, the development of quantum computers poses a severe challenge to the security of existing algorithms, especially those encryption algorithms that are based on complex mathematical problems such as large number decomposition and discrete logarithms to ensure data security. Quantum computing, with its unique parallel processing capabilities, may quickly crack traditional encryption systems such as RSA and ECC, forcing the security field to re-examine and upgrade algorithms to resist quantum attacks and ensure the security of data in the quantum era.

[0005] In order to realize a multi-party collaborative risk prediction mechanism without leaking the local data of each participant, the present invention proposes a multi-party joint risk prediction method and system based on quantum-resistant privacy computing. Summary of the invention

[0006] In order to make up for the shortcomings of the prior art, the present invention provides a simple and efficient multi-party joint risk prediction method and system based on quantum-resistant privacy computing.

[0007] The present invention is achieved through the following technical solutions:

[0008] A multi-party joint risk prediction method based on quantum-resistant privacy computing, characterized in that it includes the following steps:

[0009] Step S1: All parties involved in the joint risk analysis use the quantum-resistant digital signature algorithm standard Dilithium to construct a public key infrastructure PKI system, and issue certificates through the constructed PKI system;

[0010] In step S1, the certificate issuing CA agency calls the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root;

[0011] Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

[0012] Step S2: Each participant in the joint risk analysis uses the PKI system combined with a quantum-resistant non-interactive zero-knowledge proof system to register for a certificate and exchange the public keys of the certificates with each other;

[0013] In step S2, the implementation steps are as follows:

[0014] Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol;

[0015] Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk;

[0016] Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof;

[0017] Step S2.4: The certificate issuer CA organization uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate.

[0018] Step S3: Each participating institution in the joint risk analysis runs a quantum privacy-resistant intersection protocol based on lattice algebra, namely, the PSI protocol, according to the public key certificate registered in the system and its own local database index value, to achieve the intersection between the local database indexes of multiple participants;

[0019] In step S3, each participant sends its own certificate to other participants, and then jointly executes an interactive PSI protocol. Finally, the protocol is returned to each participant, and the sample is aligned with the index value that intersects with the database.

[0020] Step S4: Each participating institution in the joint risk analysis executes a pre-computation algorithm of the quantum-resistant secure multi-party computing protocol to generate a series of beaver triplets;

[0021] In step S4, the quantum-resistant secure multi-party computing protocol is the SPDZ protocol, and each participant finally obtains a secret sharing triplet ([a], [b], [c=ab]), called a beaver triplet, which is used for nonlinear operations in the distributed computing risk prediction function F.

[0022] Step S5: Each participating institution in the joint risk analysis uses a universal secure multi-party computing protocol (SPDZ protocol) to combine the beaver triples generated in the pre-computation phase with the index value after intersection of the local database, jointly calculates the risk prediction function F, and finally returns the risk prediction index to the designated participant.

[0023] In step S5, when calculating the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

[0024] A multi-party joint risk prediction system based on quantum-resistant privacy computing, including:

[0025] The certificate issuance module is responsible for constructing a public key infrastructure (PKI) system using the quantum-resistant digital signature algorithm standard Dilithium, and issuing certificates through the constructed PKI system to all parties involved in the joint risk analysis;

[0026] The certificate issuance module helps the certificate issuer CA agency to call the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root;

[0027] Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

[0028] The certificate application and exchange module is responsible for helping all parties involved in the joint risk analysis to register for certificates and exchange the public keys of the certificates using the PKI system combined with the quantum-resistant non-interactive zero-knowledge proof system;

[0029] The steps of the certificate application and exchange module to implement certificate registration application and mutual exchange are as follows:

[0030] Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol;

[0031] Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk;

[0032] Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof;

[0033] Step S2.4: The certificate issuer CA organization uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate.

[0034] The privacy intersection module is responsible for helping each participating institution in the joint risk analysis to run the quantum-resistant privacy intersection protocol based on lattice algebra, namely the PSI protocol, according to the public key certificates registered in the system and their local database index values, to achieve the intersection between the local database indexes of multiple participants;

[0035] Each participant sends its own certificate to other participants through the privacy intersection module, and then jointly executes an interactive PSI protocol. The final protocol is returned to each participant, and the sample is aligned with the index value that intersects with the database.

[0036] The pre-computation module is responsible for executing the pre-computation algorithm of the quantum-resistant secure multi-party computing protocol and generating a series of beaver triplets for each participating institution in the joint risk analysis;

[0037] In the risk prediction module, all participating institutions in the joint risk analysis use the general secure multi-party computing protocol (SPDZ protocol) to combine the beaver triples generated in the pre-computation phase with the index value after intersection of the local database, jointly calculate the risk prediction function F, and finally return the risk prediction index to the designated participant.

[0038] The quantum-resistant secure multi-party computing protocol is the SPDZ protocol, where each participant finally obtains a secret sharing triple ([a], [b], [c = ab]), called a beaver triple, which is used for nonlinear operations in the distributed computing risk prediction function F.

[0039] When the risk prediction module calculates the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

[0040] A computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the above methods.

[0041] The beneficial effects of the present invention are as follows: the multi-party joint risk prediction method and system based on quantum privacy-resistant computing adopts an efficient privacy intersection algorithm that can resist quantum computer attacks, combined with a general quantum secure multi-party computing protocol to perform distributed secure reading and joint computing of multi-party data, which can ensure the security of data of all parties under the attack of quantum computers. At the same time, all parties can perform efficient joint risk prediction, enrich the number of eigenvalues ​​in the risk prediction model, and improve the accuracy of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Attached Figure 1 The figure is a schematic diagram of the implementation of the quantum-resistant PKI system based on the Dilithium algorithm and the method for applying for and exchanging certificates of participating parties in the present invention.

[0044] Attached Figure 2 Schematic diagram of the privacy intersection method based on the quantum-resistant PKI system of the present invention.

[0045] Attached Figure 3 This is a schematic diagram of the quantum-resistant joint risk prediction method based on the SPDZ protocol of the present invention. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0047] By combining cryptographic algorithms with existing risk prediction models, we can achieve the goal of improving model accuracy while making data available but invisible and keeping the original data within the domain.

[0048] The multi-party joint risk prediction method based on quantum-resistant privacy computing includes the following steps:

[0049] Step S1: All parties involved in the joint risk analysis use the quantum-resistant digital signature algorithm standard Dilithium to construct a public key infrastructure PKI system, and issue certificates through the constructed PKI system;

[0050] In step S1, the certificate issuing CA agency calls the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root;

[0051] Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

[0052] Step S2: Each participant in the joint risk analysis uses the PKI system combined with a quantum-resistant non-interactive zero-knowledge proof system to register for a certificate and exchange the public keys of the certificates with each other;

[0053] In step S2, the implementation steps are as follows:

[0054] Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol;

[0055] Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk;

[0056] Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof;

[0057] Step S2.4: The certificate issuer CA uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate. Figure 1 .

[0058] Step S3: Each participating institution in the joint risk analysis runs a quantum privacy-resistant intersection protocol based on lattice algebra, namely, the PSI protocol, according to the public key certificate registered in the system and its own local database index value, to achieve the intersection between the local database indexes of multiple participants;

[0059] In step S3, each participant sends its own certificate to other participants, and then jointly executes an interactive PSI protocol. The final protocol is returned to each participant, and the index value that intersects with the database is aligned with the sample. For details, please see the attached Figure 2 .

[0060] Step S4: Each participating institution in the joint risk analysis executes a pre-computation algorithm of the quantum-resistant secure multi-party computing protocol to generate a series of beaver triplets;

[0061] In step S4, the quantum-resistant secure multi-party computing protocol is the SPDZ protocol, and each participant finally obtains a secret sharing triplet ([a], [b], [c=ab]), called a beaver triplet, which is used for nonlinear operations in the distributed computing risk prediction function F.

[0062] Step S5: Each participating institution in the joint risk analysis uses a universal secure multi-party computing protocol (SPDZ protocol) to combine the beaver triples generated in the pre-computation phase with the index value after intersection of the local database, jointly calculates the risk prediction function F, and finally returns the risk prediction index to the designated participant.

[0063] In step S5, when calculating the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

[0064] The multi-party joint risk prediction system based on quantum privacy-resistant computing includes:

[0065] The certificate issuance module is responsible for constructing a public key infrastructure (PKI) system using the quantum-resistant digital signature algorithm standard Dilithium, and issuing certificates through the constructed PKI system to all parties involved in the joint risk analysis;

[0066] The certificate issuance module helps the certificate issuer CA agency to call the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root;

[0067] Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

[0068] The certificate application and exchange module is responsible for helping all parties involved in the joint risk analysis to register for certificates and exchange the public keys of the certificates using the PKI system combined with the quantum-resistant non-interactive zero-knowledge proof system;

[0069] The steps of the certificate application and exchange module to implement certificate registration application and mutual exchange are as follows:

[0070] Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol;

[0071] Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk;

[0072] Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof;

[0073] Step S2.4: The certificate issuer CA organization uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate.

[0074] The privacy intersection module is responsible for helping each participating institution in the joint risk analysis to run the quantum-resistant privacy intersection protocol based on lattice algebra, namely the PSI protocol, according to the public key certificates registered in the system and their local database index values, to achieve the intersection between the local database indexes of multiple participants;

[0075] Each participant sends its own certificate to other participants through the privacy intersection module, and then jointly executes an interactive PSI protocol. The final protocol is returned to each participant, and the sample is aligned with the index value that intersects with the database.

[0076] The pre-computation module is responsible for executing the pre-computation algorithm of the quantum-resistant secure multi-party computing protocol and generating a series of beaver triplets for each participating institution in the joint risk analysis;

[0077] The quantum-resistant secure multi-party computing protocol is the SPDZ protocol, and each participant finally obtains a secret sharing triplet ([a], [b], [c = ab]), called the beaver triplet, which is used for nonlinear operations in the distributed computing risk prediction function F.

[0078] In the risk prediction module, all participating institutions in the joint risk analysis use the general secure multi-party computing protocol (SPDZ protocol) to combine the beaver triples generated in the pre-computation phase with the index value after intersection of the local database, jointly calculate the risk prediction function F, and finally return the risk prediction index to the designated participant.

[0079] When the risk prediction module calculates the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

[0080] The computer-readable storage medium stores one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device performs any one of the above methods.

[0081] This multi-party joint risk prediction method and system based on quantum privacy-resistant computing aims to solve the problems of insufficient data samples and single eigenvalues ​​in current risk models, as well as the threat of quantum computer development to users' remote data transmission. It uses an efficient privacy intersection algorithm and secure multi-party computing protocol that are resistant to quantum computer attacks to perform distributed secure reading and joint computing of multi-party data, and designs a multi-party joint risk prediction model that can resist quantum computer attacks, which can ensure the security of data of all parties under the attack of quantum computers. At the same time, all parties can conduct efficient joint risk prediction, enriching the number of eigenvalues ​​and sample size in the risk prediction model, and improving the accuracy of prediction.

[0082] In terms of application scenarios, this method is not only applicable to fields that require a high degree of data protection, such as finance, medical care, and transportation, but can also be expanded to emerging fields such as smart manufacturing and smart cities, providing these fields with more secure and efficient data processing and analysis capabilities. Through multi-party computing, the credit status of individuals can be evaluated and predicted while protecting personal privacy, providing strong support for the construction of a social credit system. Through more accurate and timely risk prediction, enterprises can make more scientific decision-making plans, optimize resource allocation, and improve operational efficiency and market competitiveness.

[0083] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-party joint risk prediction method based on quantum-resistant privacy computing, characterized by: The following steps are involved: Step S1: All parties involved in the joint risk analysis use the quantum-resistant digital signature algorithm standard Dilithium to construct a public key infrastructure PKI system, and issue certificates through the constructed PKI system; Step S2: Each participant in the joint risk analysis uses the PKI system combined with a quantum-resistant non-interactive zero-knowledge proof system to register for a certificate and exchange the public keys of the certificates with each other; Step S3: Each participating institution in the joint risk analysis runs a quantum privacy-resistant intersection protocol based on lattice algebra, namely, the PSI protocol, according to the public key certificate registered in the system and its own local database index value, to achieve the intersection between the local database indexes of multiple participants; Step S4: Each participating institution in the joint risk analysis executes a pre-computation algorithm of the quantum-resistant secure multi-party computing protocol to generate a series of beaver triplets; Step S5: Each participating institution in the joint risk analysis uses a general secure multi-party computing protocol, combines the beaver triples generated in the pre-computation phase with the index value after the intersection of the local database, jointly calculates the risk prediction function F, and finally returns the risk prediction index to the designated participant.

2. The multi-party joint risk prediction method based on quantum-resistant privacy computing according to claim 1 is characterized in that: In step S1, the certificate issuing CA agency calls the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root; Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

3. The multi-party joint risk prediction method based on quantum-resistant privacy computing according to claim 1 is characterized in that: In step S2, the implementation steps are as follows: Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol; Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk; Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof; Step S2.4: The certificate issuer CA organization uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate.

4. The multi-party joint risk prediction method based on quantum-resistant privacy computing according to claim 1 is characterized in that: In step S3, each participant sends its own certificate to other participants, and then jointly executes an interactive PSI protocol. Finally, the protocol is returned to each participant, and the sample is aligned with the index value that intersects with the database.

5. The multi-party joint risk prediction method based on quantum-resistant privacy computing according to claim 1 is characterized in that: In step S4, the quantum-resistant secure multi-party computing protocol is the SPDZ protocol, and each participant finally obtains a secret sharing triplet ([a], [b], [c=ab]), called a beaver triplet, which is used for nonlinear operations in the distributed computing risk prediction function F; In step S5, when calculating the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

6. A multi-party joint risk prediction system based on quantum-resistant privacy computing, characterized by: include: The certificate issuance module is responsible for constructing a public key infrastructure (PKI) system using the quantum-resistant digital signature algorithm standard Dilithium, and issuing certificates through the constructed PKI system to all parties involved in the joint risk analysis; The certificate application and exchange module is responsible for helping all parties involved in the joint risk analysis to register for certificates and exchange the public keys of the certificates using the PKI system combined with the quantum-resistant non-interactive zero-knowledge proof system; The privacy intersection module is responsible for helping each participating institution in the joint risk analysis to run the quantum-resistant privacy intersection protocol based on lattice algebra, namely the PSI protocol, according to the public key certificates registered in the system and their local database index values, to achieve the intersection between the local database indexes of multiple participants; Each participant sends its own certificate to other participants through the privacy intersection module, and then jointly executes an interactive PSI protocol. The final protocol is returned to each participant, and the sample is aligned with the index value that intersects the database; The pre-computation module is responsible for executing the pre-computation algorithm of the quantum-resistant secure multi-party computing protocol and generating a series of beaver triplets for each participating institution in the joint risk analysis; In the risk prediction module, all participating institutions in the joint risk analysis use a universal secure multi-party computing protocol, combined with the beaver triples generated in the pre-computation phase and the index value after the intersection of the local database, to jointly calculate the risk prediction function F, and finally return the risk prediction index to the designated participant.

7. The multi-party joint risk prediction system based on quantum privacy-resistant computing according to claim 6 is characterized in that: The certificate issuance module helps the certificate issuer CA agency to call the key generation algorithm of the Dilithium algorithm to generate a public key pkCA and a private key skCA as a trust root; Then use the private key skCA to self-sign your own public key pkCA as the certificate of the certificate issuer CA in the PKI system.

8. The multi-party joint risk prediction system based on quantum privacy-resistant computing according to claim 6 is characterized in that: The steps of the certificate application and exchange module to implement certificate registration application and mutual exchange are as follows: Step S2.1, each participant calls the key generation algorithm of the Dilithium algorithm to generate a public key pk and a private key sk as the key for subsequent execution of the protocol; Step S2.2, each participant uses a quantum-resistant zero-knowledge proof algorithm to prove that each participant has the private key sk corresponding to the public key pk; Step S2.3, each participant sends the public key pk and the certificate m to the certificate issuing CA organization; after receiving the certificate, the certificate issuing CA organization runs the verification algorithm of the zero-knowledge proof algorithm to verify the correctness of the proof; Step S2.4: The certificate issuer CA organization uses its own private key skCA to sign the application public key pk and issues it to the participant as a certificate.

9. The multi-party joint risk prediction system based on quantum privacy-resistant computing according to claim 6 is characterized in that: The quantum-resistant secure multi-party computing protocol is the SPDZ protocol, where each participant finally obtains a secret sharing triple ([a], [b], [c = ab]), called a beaver triple, which is used for nonlinear operations in the distributed computing risk prediction function F. When the risk prediction module calculates the joint risk function F, the nonlinear part is converted into a linear operation using the pre-calculated beaver triples; in the secure multi-party computing protocol, the linear part is directly calculated by the participants locally.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions which, when executed by a computing device, cause the computing device to perform any one of the methods according to claims 1 to 5 .

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