## 8 Conclusions and Future Work  
### Work Summary  
Privacy computing is a technology framework aimed at protecting individual privacy during the process of data use and sharing. It ensures that data can still be effectively utilized without disclosing specific content through various algorithms and protocols. Among many applications of privacy computing, Privacy Set Intersection (PSI-CA) is a common requirement, which allows two or more parties to compute the cardinality of the intersection without revealing their private data.  
Traditional PSI-CA protocols are primarily designed for cases where datasets are relatively balanced in size, which often does not apply in real scenarios. In many practical situations, the size disparity between participants' datasets is significant, necessitating the use of unbalanced PSI-CA protocols. These protocols are specifically designed to handle such disparities, optimizing computational efficiency and privacy protection to cater to a wider range of practical needs. By adopting unbalanced PSI-CA protocols, not only is the processing efficiency improved, but more precise control over data protection is also offered, thus finding broader application in various data-sensitive industries. This paper proposes three protocols: the unbalanced PSI-CA protocol based on Cuckoo filter, the unbalanced PSI-CA protocol based on single-cloud assistance, and the unbalanced PSI-CA protocol based on dual-cloud assistance. Here, the unbalanced PSI-CA protocol based on Cuckoo filter addresses the performance issues of traditional PSI-CA protocols in handling unbalanced datasets. On this basis, the unbalanced PSI-CA protocol based on single-cloud assistance transfers most of the computational and storage burdens from the small health app developer to the cloud, enhancing practicality. Faced with the possibility of collusion attacks, the unbalanced PSI-CA protocol based on dual-cloud assistance employs security mechanisms such as homomorphic encryption to effectively resist these attacks. The main contributions of this paper are summarized as follows:  
1. Addressing the shortcomings of traditional PSI-CA protocols when dealing with significant data size disparities among participants, this paper proposes the first protocol, namely the unbalanced PSI-CA protocol based on Cuckoo filter.
2. Given the complexities of cryptographic operations and storage demands of the small health app developer in the unbalanced PSI-CA protocol based on Cuckoo filter, this paper introduces an unbalanced PSI-CA protocol based on single-cloud assistance.  
This protocol effectively transfers the majority of computational and storage burdens from the small health app developer to the cloud.
3. In response to potential collusion between the cloud and large medical institution in the unbalanced PSI-CA protocol based on single-cloud assistance, this paper proposes a unbalanced PSI-CA protocol based on dual-cloud assistance with security mechanisms like homomorphic encryption, which effectively prevents collusion attacks while offloading computational and storage burdens.
4. In view of the practical problems of the unbalanced PSI-CA protocol based on dual-cloud assistance, this paper also designs a PSI-CA network and a data update mode tailored for the unbalanced PSI-CA protocol based on dual-cloud assistance.