Data Processing Method for Wearable Devices Based on Cloud Computing

Abstract

The present invention discloses a method for processing wearable device data based on cloud computing, including: the data owner defines its data access policy, encrypts its wearable device data using the variant Paillier homomorphic encryption public key, and sends it to the service provider; 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; performs a masking process on the aggregation result, and sends the masked aggregation data to the computing party; 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; the service provider performs a demasking process on the masked data, and sends the aggregated data after removing the mask to the data requester; the data requester decrypts the aggregated data using its attribute-based encryption private key according to the access policy defined by the data owner. The present invention can not only protect user privacy, but also support flexible data aggregation and sharing.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and more specifically, to a method for processing wearable device data 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 have become a challenge. Existing solutions often cannot simultaneously meet the requirements of data privacy protection and flexible data sharing. Summary of the Invention

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

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

[0005] S1: Build a cloud computing service system, 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 agency;

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

[0007] 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;

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

[0009] 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;

[0010] S6: The service provider performs a masking process on the aggregation result and sends the masked aggregation data to the computing party;

[0011] S7: The computing party decrypts the masked data using a strong decryption key, performs data calculation and processing, 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's masking of the aggregation result includes:

[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 the encrypted random numbers;

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

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

[0029] The computing party decrypts the masked aggregated data ciphertext using the variant Paillier homomorphic encryption private key to obtain the masked aggregated data plaintext for further processing or analysis;

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

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

[0032] The computing party encrypts the decrypted aggregated data using the new Paillier public key to 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 encrypts the new Paillier private key using the access policy defined by the data owner through ciphertext-policy attribute-based encryption 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 demasks the masked data and sends the de-masked aggregated data to the data requester, including:

[0037] After receiving the encrypted aggregated data and the encrypted Paillier private key returned by the computing party, the service provider demasks the encrypted aggregated data, removes the previously added random numbers, and obtains the ciphertext of the final aggregated data;

[0038] The service provider sends the de-masked encrypted aggregated data and the encrypted Paillier private key to the authorized data requester.

[0039] Optionally, in step 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, including:

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

[0041] The data requester uses the obtained decrypted Paillier private key to decrypt the encrypted aggregated data ciphertext to recover the original aggregated data.

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

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

[0044] In the method of the present invention, the data owner encrypts its data using the variant Paillier homomorphic encryption public key to generate encrypted data, and sends the encrypted data to the service provider. The service provider receives the data aggregation request and performs corresponding homomorphic operations according to the request type. The service provider masks the aggregation result and sends the masked aggregated data to the computing party. The computing party decrypts the masked data using the strong decryption key, re-encrypts it and sends it back to the service provider. The service provider performs de-masking processing and sends the processing result to the data requester. The data requester decrypts the aggregated data using its attribute-based encryption private key according to the access policy defined by the data owner. The present invention combines multi-key homomorphic encryption technology and attribute-based encryption technology to provide a solution that both protects user privacy and supports flexible data sharing. User data remains encrypted during the collection, transmission, and processing process, effectively preventing data leakage. The data owner can flexibly share data with others according to its 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 on resource-constrained devices (such as wearable devices) is reduced. By using homomorphic encryption technology, even in the case of an attack on the cloud service provider, data can be protected from unauthorized access.

[0045] Furthermore, this method strengthens the data owner's control over the data, including data encryption, access, and sharing. This method combines multi-key partial 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: the data owner accessing their own data, the data requester accessing the data of a single data owner, and the data requester accessing the data of multiple data owners. This method realizes an efficient data processing flow, enabling the data owner to encrypt the data only once to support various data processing and sharing requirements. 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 obvious from the accompanying drawings incorporated herein and the subsequent detailed description, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed description. These accompanying drawings and detailed description are used together to explain the specific principles of the present invention. Brief Description of the Drawings

[0047] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0048] Figure 1 A flowchart showing the steps of a method for processing data of a wearable device based on cloud computing according to the present invention is shown. Detailed Description of the Embodiments

[0049] The present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the 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] Embodiment

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

[0052] 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;

[0053] Specifically, the cloud computing service system architecture established in this embodiment includes the following key components: data owners, service providers, computing parties, data requesters, and key management agencies. 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: Encrypt the data collected by the wearable device using the variant Paillier homomorphic encryption public key.

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

[0057] Access policy definition: Define the single data owner access policy (APS) and the multi-data owner access policy (APM) to control access to its data.

[0058] Data access: Request access to the aggregation result of its own encrypted data as needed.

[0059] The functions of the service provider include:

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

[0061] Data aggregation: Perform homomorphic operations (such as addition) to aggregate the encrypted data according to the request.

[0062] Masking processing: Perform masking processing on the aggregation result to protect the privacy of the data during the calculation process.

[0063] Request processing: Receive the access request from the data requester and process it according to the access policy defined by the data owner.

[0064] Data transmission: Send the masked aggregated data to the computing party, and send the unmasked aggregated result 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 the encrypted data from the data owner and performing operations such as data aggregation according to the request. The service provider performs homomorphic operations, such as addition, to aggregate the 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, and this process involves data transmission and storage, both of which are part of the cloud computing service.

[0066] The functions of the computing party include:

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

[0068] Data re - encryption: Re - encrypt the decrypted data and use the Ciphertext - Policy Attribute - Based Encryption (CP - ABE) technique to encrypt the Paillier private key according to the access policy defined by the data owner.

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

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

[0071] The functions of the data requester include:

[0072] Access request: Request the aggregated result of specific data as needed.

[0073] Data decryption: Use its own Attribute - Based Encryption private key to decrypt the aggregated data to obtain the final aggregated result.

[0074] The data requester can also include the data owner.

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

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

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

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

[0079] S2: The key management authority 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 - 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 authority 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 and 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 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 and 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 realizing 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 already exists and will not be elaborated in this article.

[0083] This step can also provide a unique encryption key for each data owner, enhancing the personal privacy protection of the data. 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, 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.

[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 adopts an encrypted transmission method to protect the privacy and integrity of data during the sending process.

[0092] 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;

[0093] In this step, 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;

[0094] The service provider performs corresponding homomorphic operations according to 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 additive homomorphic operation and multiplies the corresponding ciphertexts.

[0096] 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.

[0097] Specifically, data aggregation refers to the process of gathering data from multiple sources together 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 on encrypted data without the need for decryption, which is applicable to 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's masking of the aggregation result includes:

[0100] The service provider generates a random number for the data of each data owner;

[0101] 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;

[0102] 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.

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

[0104] S7: The computing party uses the strong decryption key to decrypt the masked data, re-encrypts it after computing and processing the data, and sends it back to the service provider;

[0105] In this step, when the computing party uses the strong decryption key to decrypt the masked data, it includes:

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

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

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

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

[0110] The computing party obtains the access policy defined by the data owner, and 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 ciphertext-policy attribute-based encryption 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., multiplied after encryption with a random number), the computing party can still decrypt the masked data to 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 using the Paillier public key to encrypt the masked data and using ciphertext-policy attribute-based encryption to encrypt the Paillier private key with the access policy defined by the data owner, ensuring that only data requesters who meet the access policy can decrypt and access the data, achieving fine-grained access control.

[0114] S8: The service provider performs de-masking processing on the masked data and sends the de-masked aggregated data to the data requester;

[0115] This step specifically includes:

[0116] After the service provider receives the encrypted aggregated data and the encrypted Paillier private key returned by the computing party, it performs a de-masking process on the encrypted aggregated data, removes the previously added random numbers, and obtains the ciphertext of the final aggregated data.

[0117] The service provider sends the de-masked aggregated data ciphertext and the encrypted Paillier private key to the authorized data requester.

[0118] Specifically, this step ensures that only the authorized data requester can access the final aggregated result.

[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 decrypts the encrypted Paillier private key ciphertext using its attribute-based encryption private key to obtain the Paillier private key.

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

[0123] Specifically, the data requester decrypts the aggregated data using the ABE private key according to the single data owner access policy (APS) or multi-data owner access policy (APM) defined by the data owner. The data owner is allowed to set flexible access policies according to needs, whether for data of a single or multiple data owners, thus achieving more precise data sharing and privacy protection, while implementing role-based access control to ensure data compliance and security.

[0124] It should be noted that in the present invention, homomorphic encryption allows arithmetic operations to be directly performed on ciphertexts, which means that it becomes 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. What the computing party decrypts from the masked data is the data plaintext with the mask, which can perform necessary data processing while ensuring data privacy. By using homomorphic encryption, even if the data is processed in the encrypted state, its privacy can be protected. This is because homomorphic encryption allows calculations to be performed on the encrypted data and obtain an encrypted result, and the original data is obtained after decryption, which means that even if the data is processed in the encrypted state, its privacy can be protected. In the data sharing scenario, it is often necessary to provide sensitive data to other parties for calculation and analysis, but at the same time, the privacy of the data needs to be protected. By using the homomorphic encryption algorithm, the data owner can encrypt the data and share it with other parties, and other parties can perform calculations and analysis without decrypting it, thus ensuring the security of data privacy. Further, the homomorphic encryption algorithm can achieve secure data sharing. Under the premise of protecting personal privacy, it can effectively perform data analysis and implement relevant applications. By using the homomorphic encryption algorithm, 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, and at the same time, effective analysis of the data can be achieved.

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

[0126] 1. Enhanced data privacy:

[0127] The user data remains encrypted during the collection, transmission, and processing processes, effectively preventing data leakage.

[0128] 2. Flexible data sharing:

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

[0130] 3. Reduced computational burden:

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

[0132] 4. Improved system security:

[0133] By using homomorphic encryption technology, even in the case of an attack on the cloud service provider, the data can be protected from unauthorized access.

[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 both protects user privacy and supports flexible data processing and sharing, and has important practical application value and technical significance.

[0137] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A data processing method for a wearable device 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 encrypts its wearable device data using the variant Paillier homomorphic encryption public key 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 performs a masking process on the aggregation result and sends the masked aggregation data to the computing party; S7: The computing party decrypts the masked data using a strong decryption key, performs data calculation and processing, re-encrypts it, and sends it back to the service provider; The computing party decrypts the masked data using the strong decryption key specifically includes: The computing party decrypts the ciphertext of the masked aggregation data using the variant Paillier homomorphic encryption private key to obtain the masked aggregation data plaintext for further processing or analysis; S8: The service provider performs a de-masking process 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 method for processing wearable device data based on cloud computing according to claim 1, wherein 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 the modular inverse of L(g λ mod n 2 ), and L represents the modular inverse operation.

3. The method for processing wearable device data based on cloud computing according to claim 2, wherein In step S3, the data owner encrypts its wearable device data using the variant Paillier homomorphic encryption public key, including: The data owner encrypts the data m collected by its wearable device using the public key (n, g), selects a random number r during the encryption process, 0 < r < n, and calculates the ciphertext c: c = g m r n mod n 2 ; where 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, the modulo operation is performed on n 2 to obtain the final ciphertext c.

4. The method for processing data of a wearable device 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 method for processing wearable device data based on cloud computing according to claim 1, wherein In step S6, the service provider performs a masking process on 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 masked ciphertext of the aggregated data.

6. The method for processing wearable device data based on cloud computing according to claim 1, wherein, In step S7, the computing party re-encrypts the data after computing and processing and sends it back to the service provider, including: The computing party obtains a new Paillier public key and private key from the key management agency; The computing party encrypts the decrypted aggregated data using the new Paillier public key to obtain a new ciphertext; The computing party obtains the access policy defined by the data owner, and the access policy is a single data owner access policy or a multi-data owner access policy; The computing party encrypts the new Paillier private key by means of ciphertext-policy attribute-based encryption using the access policy defined by the data owner 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.

7. The method for processing wearable device data based on cloud computing according to claim 6, wherein In step S8, the service provider performs de-masking processing on the masked data and sends the de-masked aggregated data to the data requester, including: After receiving the encrypted aggregated data and the encrypted Paillier private key returned by the computing party, the service provider performs de-masking processing on the encrypted aggregated data, removes the previously added random numbers, and obtains the final ciphertext of the aggregated data; The service provider sends the de-masked ciphertext of the aggregated data and the encrypted Paillier private key to the authorized data requester.

8. The method for processing wearable device data based on cloud computing according to claim 7, wherein, In step S9, the data requester decrypts the aggregated data using the attribute-based encryption private key according to the access policy defined by the data owner, including: The data requester decrypts the ciphertext of the encrypted Paillier private key using its attribute-based encryption private key to obtain the Paillier private key; The data requester uses the decrypted Paillier private key to decrypt the ciphertext of the aggregated data to recover the original aggregated data.

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

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