Wearable device data processing method based on cloud computing

Abstract

The invention discloses a wearable device data processing method based on cloud computing, and the method comprises the steps: a data owner defines a data access strategy, encrypts the data of a wearable device through employing a variant Paillier homomorphic encryption public key, and transmits the data to a service provider; the service provider receives a data aggregation request from the data requester and executes corresponding homomorphic operation according to the request type to aggregate the encrypted data; performing mask processing on the aggregation result, and sending the masked aggregation data to a calculation party; the calculation party uses the strong decryption key to decrypt the mask data, performs calculation processing on the data, re-encrypts the data and sends the data back to the service provider; the service provider carries out mask removal processing on the mask data and sends the aggregated data after mask removal to the data requester; and the data requester decrypts the aggregated data by using the attribute-based encryption private key of the data requester according to an access policy defined by the data owner. According to the method, the privacy of the user can be protected, and flexible data aggregation and sharing can be supported.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a wearable device data processing method based on cloud computing. Background Art

[0002] With the rapid development of wearable device technology, a large amount of personal health and activity data is collected and stored in the cloud. However, the privacy protection and secure sharing of this data has become a challenge. Existing solutions often cannot meet the needs of data privacy protection and flexible data sharing at the same time. Summary of the invention

[0003] The purpose of the present invention is to propose a wearable device data processing method based on cloud computing, which can protect user privacy and support flexible data aggregation and sharing.

[0004] To achieve the above object, the present invention proposes a wearable device data processing method based on cloud computing, comprising:

[0005] S1: Building a cloud computing service system, which includes data owners, data requesters, and cloud computing platforms. The cloud computing platform integrates service providers, computing parties, and key management agencies.

[0006] S2: The key management agency generates a variant Paillier homomorphic encryption public-private key pair for each data owner, and generates an attribute-based encryption public-private key pair for each data requester, distributes the variant Paillier homomorphic encryption public-private key to the data owner, and distributes the attribute-based encryption public-private key to the data requester;

[0007] S3: The data owner defines its data access policy and encrypts its wearable device data using a variant of the Paillier homomorphic encryption public key to generate encrypted data;

[0008] S4: The data owner sends the encrypted data to the service provider;

[0009] S5: The service provider receives the data aggregation request from the data requester and performs corresponding homomorphic operations to aggregate the encrypted data according to the request type;

[0010] S6: The service provider masks the aggregation result and sends the masked aggregated data to the computing party;

[0011] S7: The computing party decrypts the masked data using a strong decryption key, processes the data, and re-encrypts it and sends it back to the service provider;

[0012] S8: The service provider performs de - masking on the masked data and sends the aggregated data after removing the mask to the data requester;

[0013] S9: The data requester decrypts the aggregated data using its attribute - based encryption private key according to the access policy defined by the data owner.

[0014] Optionally, in step S2, the key management agency generates variant Paillier homomorphic encryption public - private key pairs for each data owner, including:

[0015] The key management agency generates variant Paillier homomorphic encryption public - private key pairs for each data owner, where the public key is (n, g), n is the product of two large prime numbers, g is a generator, and the private key is (λ, μ), λ is the least common multiple of two large prime numbers p and q, and μ is the modular inverse of L(g λ mod n 2 ) and L represents the modular inverse operation.

[0016] Optionally, in step S3, the data owner encrypts its wearable device data using the variant Paillier homomorphic encryption public key, including:

[0017] The data owner encrypts the data m collected by its wearable device using the public key (n, g). In the encryption process, a random number r (satisfying 0 < r < n) is selected, and the ciphertext c is calculated:

[0018] c = g m r n mod n 2 ;

[0019] where, g m is the encrypted form of the plaintext m, r n is the encrypted form of the random number r, and after multiplying the two and taking the modulus of n 2 the final ciphertext c is obtained.

[0020] Optionally, in step S5, the data aggregation request includes: aggregating the sum of data of multiple data owners calculating a fixed multiple of the sum of data of a certain data owner;

[0021] The service provider performs corresponding homomorphic operations according to the request type to aggregate the encrypted data, including:

[0022] When the data aggregation request is to aggregate the sum of data of multiple data owners, the service provider performs an additive homomorphic operation and multiplies the corresponding ciphertexts.

[0023] When the data aggregation request is to calculate a fixed multiple of the sum of data of a certain data owner, the service provider performs a scalar multiplication homomorphic operation and raises the corresponding ciphertext to a power.

[0024] Optionally, in step S6, the service provider performs mask processing on the aggregation result, including:

[0025] The service provider generates a random number for each data owner’s data;

[0026] The service provider encrypts each random number using the data owner’s variant Paillier homomorphic encryption public key to obtain an encrypted random number.

[0027] The service provider multiplies each encrypted random number by the ciphertext of the corresponding aggregate data to obtain the masked ciphertext of the aggregate data.

[0028] Optionally, in step S7, the computing party decrypts the mask data using a strong decryption key, including:

[0029] The computing party uses the variant Paillier homomorphic encryption private key to decrypt the masked aggregate data ciphertext and obtain the masked aggregate data plaintext for further processing or analysis;

[0030] Optionally, in step S7, the computing party re-encrypts the data after computing and processing it and sends it back to the service provider, including:

[0031] The computing party obtains new Paillier public and private keys from the key management agency;

[0032] The computing party uses the new Paillier public key to encrypt the decrypted aggregate data and obtain a new ciphertext;

[0033] The computing party obtains the access policy defined by the data owner, where the access policy is a single data owner access policy or a multi-data owner access policy;

[0034] The computing party uses the access policy defined by the data owner to encrypt the new Paillier private key through the ciphertext policy attribute-based encryption method to obtain the encrypted Paillier private key;

[0035] The computing party sends the encrypted aggregated data and the encrypted Paillier private key back to the service provider.

[0036] Optionally, in step S8, the service provider performs a demasking process on the masked data and sends the demasked aggregated data to the data requester, including:

[0037] After receiving the encrypted aggregate data and the encrypted Paillier private key returned by the computing party, the service provider performs a demasking process on the encrypted aggregate data, removes the random number added previously, and obtains the final ciphertext of the aggregate data;

[0038] The service provider sends the demasked aggregate data ciphertext and the encrypted Paillier private key to the authorized data requester.

[0039] Optionally, in step S9, the data requester uses its attribute-based encryption private key to decrypt the aggregated data according to the access policy defined by the data owner, including:

[0040] The data requester uses its attribute-based encryption private key to decrypt the encrypted Paillier private key ciphertext to obtain the Paillier private key;

[0041] The data requester uses the decrypted Paillier private key to decrypt the aggregate data ciphertext to restore the original aggregate data.

[0042] Optionally, the computing party includes at least one computing node in a cloud platform; and the data requester includes the data owner.

[0043] The beneficial effects of the present invention are:

[0044] In the method of the present invention, the data owner encrypts its data using a variant Paillier homomorphic encryption public key, generates encrypted data, and sends the encrypted data to a service provider. The service provider receives a data aggregation request and performs a corresponding homomorphic operation according to the request type. The service provider performs masking on the aggregation result and sends the masked aggregated data to the computing party. The computing party decrypts the masked data using a strong decryption key, re-encrypts and sends it back to the service provider. The service provider performs demasking and sends the processing result to the data requester. The data requester uses the attribute-based encryption private key to decrypt the aggregated data according to the access policy defined by the data owner. The present invention provides a solution that protects user privacy and supports flexible data sharing by combining multi-key homomorphic encryption technology and attribute-based encryption technology. User data is always encrypted during collection, transmission and processing, effectively preventing data leakage. The data owner can flexibly share data with others according to his own wishes and set policies, while ensuring that only authorized requesters can access the data; since most of the computing and processing work is completed in the cloud, the computing burden of resource-constrained devices (such as wearable devices) is reduced. By using homomorphic encryption technology, data can be protected from unauthorized access even when the cloud service provider is attacked.

[0045] Furthermore, this method strengthens the data owner's control over the data, including encryption, access and sharing of the data. This method combines multi-key partially homomorphic encryption and ciphertext policy attribute-based encryption for the first time to support flexible data aggregation and fine-grained data sharing. At the same time, this method can adapt to three main data usage scenarios: data owners access their own data, data requesters access the data of a single data owner, and data requesters access the data of multiple data owners. This method implements an efficient data processing process, so that data owners only need to encrypt the data once to support a variety of data processing and sharing needs. This method can adapt to different application scenarios, including personalized medicine, health monitoring, etc., and has broad application prospects.

[0046] The system of the present invention has other characteristics and advantages, which will be apparent from the drawings incorporated herein and the following detailed description, or will be described in detail in the drawings incorporated herein and the following detailed description, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, in which like reference numerals generally represent like components.

[0048] Figure 1 A flowchart showing the steps of a wearable device data processing method based on cloud computing according to the present invention. DETAILED DESCRIPTION

[0049] The present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0050] Example

[0051] like Figure 1 As shown, this embodiment provides a wearable device data processing method based on cloud computing, including:

[0052] S1: Building a cloud computing service system, which includes data owners, data requesters, and cloud computing platforms. The cloud computing platform integrates service providers, computing parties, and key management agencies.

[0053] Specifically, the cloud computing service system architecture constructed in this embodiment includes the following key components: data owner, service provider, computing party, data requester, and key management agency. These components work together to achieve secure encryption, transmission, aggregation, processing, and sharing of data.

[0054] Among them, the functions of the data owner include:

[0055] Data encryption: The data collected by wearable devices is encrypted using a variant of the Paillier homomorphic encryption public key.

[0056] Data upload: Send the encrypted data to the service provider.

[0057] Access Policy Definition: Define single data owner access policy (APS) and multiple data owner access policy (APM) to control access to their data.

[0058] Data Access: Request access to aggregated results of your own encrypted data as needed.

[0059] Service Provider functions include:

[0060] Data reception: Receive encrypted data from the data owner.

[0061] Data aggregation: Upon request, perform homomorphic operations (such as addition) to aggregate encrypted data.

[0062] Masking: Mask the aggregation results to protect the privacy of the data during the calculation process.

[0063] Request processing: Receive access requests from data requesters and process them according to the access policies defined by the data owner.

[0064] Data transmission: Send the masked aggregated data to the computing party, and send the unmasked aggregated results to the data requester.

[0065] It should be noted that the service provider plays the role of cloud computing in the system, responsible for receiving encrypted data from data owners and performing operations such as data aggregation based on requests. The service provider performs homomorphic operations, such as addition, to aggregate encrypted data, which requires the computing power of the cloud computing platform. It is also responsible for sending the masked aggregated data to the computing party, a process that involves data transmission and storage, which are all part of cloud computing services.

[0066] The computing functions include:

[0067] Data Decryption: Decrypt the masked data using a strong decryption key.

[0068] Data re-encryption: The decrypted data is re-encrypted and the Paillier private key is encrypted using attribute-based encryption (CP-ABE) technology according to the access policy defined by the data owner.

[0069] Result return: The re-encrypted data and the encrypted private key are sent back to the service provider.

[0070] Specifically, the computing party uses a strong decryption key to decrypt the masked data and re-encrypts it to send it back to the service provider. This decryption and encryption process can be performed in a cloud computing environment, utilizing the computing resources of the cloud. The computing party's role includes at least one computing node in the cloud, which is responsible for processing and returning data, which is one of the core functions of cloud computing services.

[0071] The functions of a data requester include:

[0072] Access Request: Request access to aggregated results of specific data as needed.

[0073] Data decryption: Use your own attribute-based encryption private key to decrypt the aggregated data to obtain the final aggregation result.

[0074] Data requesters may also include data owners.

[0075] The functions of the key management authority include:

[0076] Key Generation: Generate and distribute Variant Paillier Homomorphic Encryption (VP-HE) and Attribute-Based Encryption (ABE) public-private key pairs.

[0077] Key management: Manage the lifecycle of all keys, including key renewal and revocation.

[0078] Specifically, the key management agency is responsible for generating and distributing keys. It is a security component in cloud services and provides key management and distribution services for cloud computing environments.

[0079] S2: The key management agency generates a variant Paillier homomorphic encryption public-private key pair for each data owner, and generates an attribute-based encryption public-private key pair for each data requester, distributes the variant Paillier homomorphic encryption public-private key to the data owner, and distributes the attribute-based encryption public-private key to the data requester;

[0080] In this step, the key management agency generates a variant Paillier homomorphic encryption public-private key pair for each data owner, including:

[0081] The key management agency generates a variant Paillier homomorphic encryption public-private key pair for each data owner. The public key is (n, g), where n is the product of two large prime numbers and g is a generator. The private key is (λ, μ), where λ is the least common multiple of two large prime numbers p and q, and μ is the modular inverse of L(g λ mod n 2 ), and L represents the modular inverse operation.

[0082] The process of generating the attribute-based encryption (ABE) public-private key pair mainly includes initial setup, key extraction, encryption algorithm, and decryption algorithm. This process ensures that only users with specific attributes can decrypt and access the encrypted data, thus achieving attribute-based access control. In this way, the ABE system can allow flexible sharing and access of data while protecting data privacy. The ABE encryption algorithm exists, and this article will not elaborate on it.

[0083] This step can also provide a unique encryption key for each data owner, enhancing the protection of personal data privacy. In addition, it combines multi-key partial homomorphic encryption and ciphertext-policy attribute-based encryption to support flexible data aggregation and fine-grained data sharing.

[0084] S3: The data owner defines its data access policy and encrypts its wearable device data using the variant Paillier homomorphic encryption public key to generate encrypted data;

[0085] In this step, when the data owner encrypts its wearable device data using the variant Paillier homomorphic encryption public key, it includes:

[0086] The data owner encrypts the data m collected by its wearable device using the public key (n, g). In the encryption process, a random number r (satisfying 0 < r < n) is selected, and the ciphertext c is calculated:

[0087] c = g m r n mod n 2 ;

[0088] Among them, g m is the encrypted form of the plaintext m, and r n is the encrypted form of the random number r. After multiplying the two and taking the modulus of n 2 , the final ciphertext c is obtained.

[0089] In this step, when the data owner encrypts its wearable device data using the variant Paillier homomorphic encryption public key, it can ensure the security of the data during transmission and storage and prevent unauthorized access.

[0090] S4: The data owner sends the encrypted data to the service provider;

[0091] This step preferably uses an encrypted transmission method to protect the privacy and integrity of the data during transmission.

[0092] S5: The service provider receives the data aggregation request from the data requester and performs corresponding homomorphic operations to aggregate the encrypted data according to the request type;

[0093] In this step, the data aggregation request includes: aggregating the sum of data of multiple data owners to calculate a fixed multiple of the sum of data of a certain data owner;

[0094] The service provider performs corresponding homomorphic operations based on the request type to aggregate the encrypted data, including:

[0095] When the data aggregation request is to aggregate the sum of data of multiple data owners, the service provider performs an addition homomorphic operation to multiply the corresponding ciphertexts.

[0096] When the data aggregation request is to calculate a fixed multiple of the sum of the data of a certain data owner, the service provider performs a scalar multiplication homomorphic operation and performs a power operation on the corresponding ciphertext.

[0097] Specifically, data aggregation refers to the process of combining data from multiple sources to form a more comprehensive and valuable data set. This step allows data to be aggregated without decryption, enhancing the flexibility of data processing and privacy protection, that is, allowing arithmetic operations such as summation to be performed on encrypted data without decryption, which is suitable for data analysis and statistics.

[0098] S6: The service provider masks the aggregation result and sends the masked aggregated data to the computing party;

[0099] In this step, the service provider performs masking processing on the aggregation result including:

[0100] The service provider generates a random number for each data owner’s data;

[0101] The service provider encrypts each random number using the data owner’s variant Paillier homomorphic encryption public key to obtain an encrypted random number.

[0102] The service provider multiplies each encrypted random number by the ciphertext of the corresponding aggregate data to obtain the masked ciphertext of the aggregate data.

[0103] Specifically, the masking process includes the service provider generating a random number, encrypting the random number using the data owner's variant Paillier homomorphic encryption public key, and multiplying it with the ciphertext of the aggregated data. This step introduces random numbers and encryption operations to prevent the computing party from obtaining plaintext data, further improving the security of the data during the computing process.

[0104] S7: The computing party decrypts the masked data using a strong decryption key, processes the data, and re-encrypts it and sends it back to the service provider;

[0105] In this step, the computing party uses a strong decryption key to decrypt the masked data, including:

[0106] The computing party uses the variant Paillier homomorphic encryption private key to decrypt the masked aggregate data ciphertext and obtain the masked aggregate data plaintext for further processing or analysis;

[0107] Furthermore, the computing party re-encrypts the data after computing and processing it and sends it back to the service provider, including:

[0108] The computing party obtains new Paillier public and private keys from the key management agency;

[0109] The computing party uses the new Paillier public key to encrypt the decrypted aggregate data and obtain a new ciphertext;

[0110] The computing party obtains the access policy defined by the data owner, where the access policy is a single data owner access policy or a multi-data owner access policy;

[0111] The computing party uses the access policy defined by the data owner to encrypt the new Paillier private key through the ciphertext policy attribute-based encryption method to obtain the encrypted Paillier private key;

[0112] The computing party sends the encrypted aggregated data and the encrypted Paillier private key back to the service provider.

[0113] In this step, due to the characteristics of homomorphic encryption, even if the data is masked (i.e., encrypted with a random number and then multiplied), the computing party can still decrypt the masked data and obtain the plaintext of the masked aggregated data. The computing party can only access the masked aggregated data to ensure the security and isolation of the data. Re-encryption includes the computing party encrypting the masked data with the Paillier public key and encrypting the Paillier private key using the ciphertext policy attribute-based encryption and the access policy defined by the data owner, ensuring that only data requesters who meet the access policy can decrypt and access the data, thus achieving fine-grained access control.

[0114] S8: The service provider demasks the masked data and sends the demasked aggregated data to the data requester;

[0115] This step specifically includes:

[0116] After receiving the encrypted aggregate data and the encrypted Paillier private key returned by the computing party, the service provider performs a demasking process on the encrypted aggregate data, removes the random number added previously, and obtains the final ciphertext of the aggregate data;

[0117] The service provider sends the demasked aggregate data ciphertext and the encrypted Paillier private key to the authorized data requester.

[0118] Specifically, this step can ensure that only authorized data requesters can access the final aggregation results.

[0119] S9: The data requester decrypts the aggregated data using its attribute-based encryption private key according to the access policy defined by the data owner.

[0120] This step specifically includes:

[0121] The data requester uses its attribute-based encryption private key to decrypt the encrypted Paillier private key ciphertext to obtain the Paillier private key;

[0122] The data requester uses the decrypted Paillier private key to decrypt the aggregate data ciphertext to restore the original aggregate data.

[0123] Specifically, the data requester uses the ABE private key to decrypt the aggregated data according to the single data owner access policy (APS) or multi-data owner access policy (APM) defined by the data owner. Data owners are allowed to set flexible access policies as needed, whether for data of a single or multiple data owners, thereby achieving more precise data sharing and privacy protection, while also implementing role-based access control to ensure data compliance and security.

[0124] It should be noted that homomorphic encryption in the present invention allows arithmetic operations to be performed directly on ciphertext, which means that it is possible to complete complex calculations without decrypting the data. In this method, the data owner encrypts the sensitive data and sends it to the cloud (i.e., the service provider and the computing party) for processing. The processing result is also in encrypted form, and only the data owner can decrypt and view the result. This feature provides strong support for data privacy protection. The computing party decrypts the masked data to obtain the masked data plaintext, which can perform necessary data processing while ensuring data privacy. By using homomorphic encryption, the privacy of the data can be protected even if it is processed in an encrypted state. This is because homomorphic encryption allows encrypted data to be calculated and the encrypted result to be obtained, and the original data is obtained after decryption, which means that even if the data is processed in an encrypted state, its privacy can be protected. In data sharing scenarios, sensitive data often needs to be provided to other parties for calculation and analysis, but the privacy of the data also needs to be protected. By using a homomorphic encryption algorithm, the data owner can encrypt the data and share it with other parties, and other parties can perform calculations and analyses without decryption, thereby ensuring the security of data privacy. Furthermore, homomorphic encryption algorithms can achieve secure data sharing, and can effectively perform data analysis and implement related applications while protecting personal privacy. By using homomorphic encryption algorithms, personal sensitive information can be encrypted and can only be decrypted and analyzed in a specific computing environment. In this way, personal privacy is effectively protected while enabling effective analysis of data.

[0125] Based on the above, the present invention has the following technical advantages:

[0126] 1. Enhanced Data Privacy:

[0127] User data is always encrypted during collection, transmission and processing, effectively preventing data leakage.

[0128] 2. Flexible data sharing:

[0129] Data owners can flexibly share data with others according to their own wishes and set policies, while ensuring that only authorized requesters can access the data.

[0130] 3. Reduce the computational burden:

[0131] Since most of the computing and processing work is done in the cloud, the computing burden on resource-constrained devices (such as wearable devices) is reduced.

[0132] 4. Improve system security:

[0133] By using homomorphic encryption technology, data can be protected from unauthorized access even if the cloud service provider is attacked.

[0134] 5. Optimize performance:

[0135] This method can adapt to different application scenarios, including personalized medicine, health monitoring, etc., and has broad application prospects.

[0136] In summary, the present invention realizes a method that protects user privacy and supports flexible data processing and sharing, which has important practical application value and technical significance.

[0137] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A wearable device data processing method based on cloud computing, characterized in that: Including: S1: Set up a cloud computing service system, where the cloud computing service system includes a data owner, a data requester, and a cloud computing platform, and the cloud computing platform integrates a service provider, a computing party, and a key management institution; S2: The key management institution generates a variant Paillier homomorphic encryption public-private key pair for each data owner, and at the same time generates an attribute-based encryption public-private key pair for each data requester, distributes the variant Paillier homomorphic encryption public and private keys to the data owners, and distributes the attribute-based encryption public and private keys to the data requesters; S3: The data owner defines its data access policy and uses the variant Paillier homomorphic encryption public key to encrypt its wearable device data to generate encrypted data; S4: The data owner sends the encrypted data to the service provider; S5: The service provider receives a data aggregation request from the data requester and performs corresponding homomorphic operations according to the request type to aggregate the encrypted data; S6: The service provider masks the aggregation result and sends the masked aggregation data to the computing party; S7: The computing party decrypts the masked data using the strong decryption key, performs data calculation and processing, re-encrypts it, and sends it back to the service provider; S8: The service provider performs de-masking on the masked data and sends the de-masked aggregation data to the data requester; S9: The data requester decrypts the aggregated data using its attribute-based encryption private key according to the access policy defined by the data owner.

2. The wearable device data processing method based on cloud computing according to claim 1, characterized in that: In step S2, the key management institution generates a variant Paillier homomorphic encryption public-private key pair for each data owner, including: The key management agency generates a variant Paillier homomorphic encryption public-private key pair for each data owner, where the public key is (n, g), n is the product of two large prime numbers, g is a generator, and the private key is (λ, μ), λ is the least common multiple of two large prime numbers p and q, and μ is L(g λ mod n 2 ), where L represents the modular inverse operation.

3. The wearable device data processing method based on cloud computing according to claim 2, characterized in that: In step S3, the data owner uses the variant Paillier homomorphic encryption public key to encrypt its wearable device data, including: The data owner encrypts the data m collected by its wearable device using the public key (n, g). In the encryption process, a random number r (satisfying 0 < r < n) is selected, and the ciphertext c is calculated: c=g m r n mod n 2 ; Among them, g m is the encrypted form of plaintext m, r n is the encrypted form of the random number r. After multiplying the two, n 2 Modulo the final ciphertext c.

4. The wearable device data processing method based on cloud computing according to claim 1, characterized in that: In step S5, the data aggregation request includes: aggregating the sum of data of multiple data owners, calculating a fixed multiple of the sum of data of a certain data owner; The service provider performs corresponding homomorphic operations according to the request type to aggregate the encrypted data, including: When the data aggregation request is to aggregate the sum of data of multiple data owners, the service provider performs an additive homomorphic operation and multiplies the corresponding ciphertexts. When the data aggregation request is to calculate a fixed multiple of the sum of data of a certain data owner, the service provider performs a scalar multiplication homomorphic operation and performs a power operation on the corresponding ciphertext.

5. The wearable device data processing method based on cloud computing according to claim 1, characterized in that: In step S6, the service provider masks the aggregation result, including: The service provider generates a random number for the data of each data owner; The service provider encrypts each random number using the variant Paillier homomorphic encryption public key of the data owner to obtain the encrypted random number; The service provider multiplies each encrypted random number by the ciphertext of the corresponding aggregated data to obtain the ciphertext of the masked aggregated data.

6. The wearable device data processing method based on cloud computing according to claim 2, characterized in that: In step S7, the computing party decrypts the masked data using the strong decryption key, including: The computing party uses the variant Paillier homomorphic encryption private key to decrypt the masked aggregate data ciphertext and obtains the masked aggregate data plaintext for further processing or analysis.

7. The wearable device data processing method based on cloud computing according to claim 6, characterized in that: In step S7, the computing party re-encrypts the data after computing and processing it and sends it back to the service provider, including: The computing party obtains new Paillier public and private keys from the key management agency; The computing party uses the new Paillier public key to encrypt the decrypted aggregate data and obtain a new ciphertext; The computing party obtains the access policy defined by the data owner, where the access policy is a single data owner access policy or a multi-data owner access policy; The computing party uses the access policy defined by the data owner to encrypt the new Paillier private key through the ciphertext policy attribute-based encryption method to obtain the encrypted Paillier private key; The computing party sends the encrypted aggregated data and the encrypted Paillier private key back to the service provider.

8. The wearable device data processing method based on cloud computing according to claim 7, characterized in that: In step S8, the service provider performs a demasking process on the masked data and sends the demasked aggregated data to the data requester, including: After receiving the encrypted aggregate data and the encrypted Paillier private key returned by the computing party, the service provider performs a demasking process on the encrypted aggregate data, removes the random number added previously, and obtains the final ciphertext of the aggregate data; The service provider sends the demasked aggregate data ciphertext and the encrypted Paillier private key to the authorized data requester.

9. The wearable device data processing method based on cloud computing according to claim 8, characterized in that: In step S9, the data requester uses its attribute-based encryption private key to decrypt the aggregated data according to the access policy defined by the data owner, including: The data requester uses its attribute-based encryption private key to decrypt the encrypted Paillier private key ciphertext to obtain the Paillier private key; The data requester uses the decrypted Paillier private key to decrypt the aggregate data ciphertext to restore the original aggregate data.

10. The wearable device data processing method based on cloud computing according to claim 1, characterized in that: The computing party includes at least one computing node in the cloud platform; the data requester includes the data owner.

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