Efficient Privacy-Preserving Secure Three-Party Sparse Data Computing
Nowadays,with the rapid development of big data and artificial intelligence(AI)tech-nology,machine learning models trained precisely on vast amounts of data and their applications have propelled an increase in productivity.However,such model training often necessitates collaboration among multiple data holders,which poses serious risks to data security and privacy,including the potential for data breaches.With the increasing awareness of private data privacy protection among the public,such collaboration encounters significant obstacles.Addressing the imperative need to establish connections among data islands while prioritizing privacy becomes a pressing concern.This challenge has spurred extensive research and development in the domain of privacy-preserving machine learning.In practical application scenarios,machine learning algorithms typically perform operations on sparse datasets.This implies that the majority of elements in the data matrix are zeros,with a relatively small number of non-zero elements actively contributing to the calculation process.Taking advantage of this data sparsity during model training reduces the data processing load,thereby enhancing computational efficiency.Cryptographic techniques have been introduced to protect data privacy.While generic secret sharing techniques address privacy concerns,the process often converts sparse data into denser forms,complicating the initially efficient computations.The existing study on secure sparse data computation involves extensive use of public-key cryptographic operations,resulting in low computational efficiency.Also,they primarily focus on scenarios involving only two parties.In fact,the computation of sparse data can be simplified to the calculations of corresponding elements at their non-zero positions.To fully exploit this characteristic for efficiency gains,we divide the problem of sparse vector multiplication into two modules:filtering and multiplication.Consequently,we propose a privacy-preserving multi-plication protocol for sparse vectors in a scenario involving three participants.Firstly,a filtering protocol is constructed based on the techniques of three-party additive replication secret sharing and pseudo-random permutation.Given specific filtering rules and the vectors to be processed,this protocol can filter the vectors according to the rules,sieving out the required vector elements.Therefore,filtering rules can be formulated based on the sparsity characteristics of vectors to sieve out elements at non-zero positions.Subsequently,we just need to perform multi-plication operations on these selected elements to complete the multiplication calculations for sparse vectors.By introducing additive homomorphic encryption technology,a privacy-preserving three-party sparse vector multiplication protocol is efficiently implemented based on the filtering protocol.We have proven the security of the protocol using the ideal/real simulation technology under the semi-honest adversary model,which ensures that apart from the output,no other private information is leaked throughout the entire process.Finally,the privacy-preserving sparse vector multiplication protocol is applied to the logistic regression model to verify its feasibility.The experiments and efficiency analysis show that,compared with the privacy-preserving sparse matrix multiplication protocol CAESAR,the proposed protocol in this paper reduces the main computing overhead from O(n)ciphertext operations to O(m)times,where n is the dimension of the vector,and m is the number of non-zero elements in the vector.In mini-batch logistic regression model training,our protocol demonstrates an efficiency improvement of 10％-30％compared to the general secure multi-party computation framework ABY3.
NETL
NSTL
万方数据
