[Problem] To make it possible to reliably conceal a label of a supervisory signal when support vector machine learning is performed. [Solution] An analysis executing apparatus that performs support vector machine learning, stores a set of learning data including a feature vector and a label encrypted using an additive homomorphic encryption scheme, which are subjected to the support vector machine learning, and performs update processing with a gradient method on the encrypted learning data using an additive homomorphic addition algorithm.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A support vector machine learning system that performs support vector machine learning, comprising: a learning data management apparatus; and a learning apparatus coupled to the learning data management apparatus, wherein the learning data management apparatus comprises: a learning data storage part that stores a set of learning data including a label and a feature vector, the set of learning data being subjected to the support vector machine learning; an encryption processing part that encrypts the label of the learning data using an additive homomorphic encryption scheme; and a learning data transmitting part that transmits encrypted learning data including the encrypted label and the feature vector to the learning apparatus, and wherein the learning apparatus comprises: a learning data receiving part that receives the encrypted learning data; and an update processing part that performs update processing with a gradient method on the encrypted learning data using an additive homomorphic addition algorithm. 2. The support vector machine learning system according to claim 1, wherein the learning data management apparatus further comprises: a dummy data addition processing part that adds dummy data to the set of learning data, and a value of the label included in the dummy data, wherein the value is set to 0. 3. The support vector machine learning system according to claim 1, wherein the learning apparatus further comprises: a coefficient generating part that generates initial values of coefficients (a1, a2, . . . , aN), which are subjected to the update processing, and wherein the update processing part generates, as a processing result of the update processing of the support vector machine learning, a set of encrypted texts {E(aiyi)|i=1, 2, . . . , N} that are calculated for i=1, 2, . . . , N based on a formula: E  ( a i  y i ) ← a i  E  ( y i ) - γ ( 2  E  ( y i ) - 2  ∑ j = 1 n  a j  E  ( y j )  〈 x i , x j 〉 ) , where xi is the feature vector, yi is the label, and E(yi) is the encrypted label using the additive homomorphic encryption scheme. 4. The support vector machine learning system according to claim 2, wherein the learning apparatus further comprises: a coefficient generating part that generates initial values of coefficients (a1, a2, . . . , aN), which are subjected to the update processing, and the update processing part generates, as a processing result of the update processing of the support vector machine learning, a set of encrypted texts {E(aiyi)|i=1, 2, . . . , N} that are calculated for i=1,2, . . . , N based on a formula: E  ( a i  y i ) ← a i  E  ( y i ) - γ ( 2  E  ( y i ) - 2  ∑ j = 1 n  a j  E  ( y j )  K  ( x i , x j ) ) , where xi is the feature vector, yi is the label, E(yi) is the encrypted label using the additive homomorphic encryption scheme. 5. The support vector machine learning system according to claim 1, wherein the update processing part performs the update processing using each of multiple coefficient groups, which are subjected to the update processing. 6. The support vector machine learning system according to claim 5, wherein the update processing part sums up processing results of the update processing for each of the multiple coefficient groups and uses the sum as the processing result. 7. A support vector machine learning system that performs support vector machine learning, comprising: a learning data storage part that stores a set of learning data including a feature vector and a label encrypted using an additive homomorphic encryption scheme, the set of learning data being subjected to the support vector machine learning; and an update processing part that performs update processing with a gradient method on the encrypted learning data using an additive homomorphic addition algorithm. 8. A support vector machine learning method of performing support vector machine learning executed by a learning data management apparatus that stores a set of learning data including a label and a feature vector, the set of learning data being subjected to the support vector machine learning, comprising: encrypting the label of the learning data using an additive homomorphic encryption scheme by the learning data management apparatus; transmitting an encrypted learning data including the encrypted label and the feature vector to a learning apparatus by the learning data management apparatus; receiving the encrypted learning data by the learning apparatus; and performing update processing with a gradient method on the encrypted learning data using an additive homomorphic addition algorithm by the learning apparatus. 9. The support vector machine learning method according to claim 8, wherein the learning data management apparatus further performs a step of adding dummy data to the set of learning data, and a value of the label included in the dummy data is set to 0. 10. The support vector machine learning system according to claim 1, wherein the learning apparatus further comprises a coefficient generating part that generates initial values of coefficients (a1, a2, . . . , aN), which are subjected to the update processing, and the update processing part generates, as a processing result of the update processing of the support vector machine learning, a set of encrypted texts {E(aiyi)|i=1, 2, . . . , N} that are calculated for i=1, 2, . . . , N based on a formula: E  ( a i  y i ) ← a i  E  ( y i ) - γ ( 2  E  ( y i ) - 2  ∑ j = 1 n  a j  E  ( y j )  K  ( x i , x j ) ) , where xi is the feature vector, yi is the label, E(yi) is the encrypted label using the additive homomorphic encryption scheme, and K is a kernel function. 11. The support vector machine learning system according to claim 2, wherein the learning apparatus further comprises a coefficient generating part that generates initial values of coefficients (a1, a2, . . . , aN), which are subjected to the update processing, and the update processing part generates, as a processing result of the update processing of the support vector machine learning, a set of encrypted texts {E(aiyi)|i=1, 2, . . . , N} that are calculated for i=1,2, . . . , N based on a formula: E  ( a i  y i ) ← a i  E  ( y i ) - γ ( 2  E  ( y i ) - 2  ∑ j = 1 n  a j  E  ( y j )  K  ( x i , x j ) ) , where xi is the feature vector, yi is the label, E(yi) is the encrypted label using the additive homomorphic encryption scheme, and K is a kernel function. 12. The support vector machine learning system according to claim 2, wherein the update processing part performs the update processing using each of multiple coefficient groups, which are subjected to the update processing. 13. The support vector machine learning system according to claim 3, wherein the update processing part performs the update processing using each of multiple coefficient groups, which are subjected to the update processing. 14. The support vector machine learning system according to claim 4, wherein the update processing part performs the update processing using each of multiple coefficient groups, which are subjected to the update processing. 15. The support vector machine learning system according to claim 12, wherein the update processing part sums up processing results of the update processing for each of the multiple coefficient groups and uses the sum as the processing result. 16. The support vector machine learning system according to claim 13, wherein the update processing part sums up processing results of the update processing for each of the multiple coefficient groups and uses the sum as the processing result. 17. The support vector machine learning system according to claim 14, wherein the update processing part sums up processing results of the update processing for each of the multiple coefficient groups and uses the sum as the processing result.