ARPA Computation Platform Q1 Update: Better Performance, Usability, and Next Steps
Over the past year and during the first quarter of 2021, ARPA developers and researchers have made tremendous efforts to upgrade our secure computation modules to achieve better performance and usability.
According to our previous business development with other financial companies and data processing entities, the most frequently used applications of privacy-preserving companies are large-scale private set intersection (PSI) and linear regression (LR). With this in mind, we prioritized optimizing these specific applications. After a thorough survey of existing frameworks and implementations, we developed a series of experiments on these tools in our current real-world project settings. By integrating additional modules into our computation system, the performance of large-scale data analysis on ARPA computation platform was improved by around 30%.
Private Set Intersection (PSI) and Linear Regression (LR)
PSI is a cryptographic technique that allows two parties to compute their sets’ intersection without revealing anything else. LR is a simple but efficient approach that shows the relationship between several factors, such as adolescent obesity and physical activities. Medical, financial, and other risk-sensitive entities are pretty dependent on such analysis tools.
However, the required massive data collection raises concerns on privacy, trade secret, and regulation. These are the ideal application scenarios of multi-party computation (MPC). ARPA secure computation platform is designed to process these analyses, and now we are improving the processing performance in the fields of asymmetry database and parallelization.
Computation performance has been tested on several public datasets such as Boston real-estate pricing, cancer linear regression, and CDC behavioral risk factor data. We extended the dataset with synthetic data to million entries. The experiment was conducted on three powerful AWS instances located in different regions. To test the asymmetric database PSI performance, we limited the inquirer’s computation power to a single thread and allocated multiple threads to the inquired party. The inquired dataset is more than ten million entries, while the inquiry dataset is comparatively smaller. The result shows that if the smaller database is one thousand times smaller than the larger one, we will get a similar performance as plaintext computation. As for LR, thanks to the fixed-point arithmetic and instruction vectorization we employed, the throughput raised around 35% because of the drastic reduction in communication overhead.
Next, we will conduct gene sequence alignment experiments on the Complete Genomics public dataset. The datasets for previous performance tests are mainly synthetic, and we would like to perform a real-world million-entry data analysis such as pairwise genetic alignment. This kind of application can help with disease risk evaluation without compromising patients’ privacy.
There is no doubt that we are off to a good start in 2021. We will release more articles and updates on our progress soon, stay tuned.
ARPA is a blockchain-based solution for privacy-preserving computation, enabled by Multi-Party Computation (“MPC”). Founded in April 2018, the goal of ARPA is to separate data utility from ownership and enable data renting. ARPA’s MPC protocol creates ways for multiple entities to collaboratively analyze data and extract data synergies while keeping each party’s data input private and secure. ARPA allows secret sharing of private data, and the correctness of computation is verifiable using information-theoretic Message Authentication Code (MAC).
Developers can build privacy-preserving dApps on blockchains compatible with ARPA. Some immediate use cases include credit anti-fraud, secure data wallet, precision marketing, joint AI model training, key management systems, etc. For example, banks using the ARPA network can share their credit blacklist for risk management purposes without exposing their customer data or privacy.
Team members have worked at leading institutions such as Google, Amazon, Huawei, Fosun, Tsinghua University, Fidelity Investments. ARPA is currently assisting the China Academy of Information and Communications Technology in setting the national standard for secure multi-party computation. ARPA is a corporate member of MPC Alliance and IEEE and is in partnership with fortune 500 companies to implement proofs-of-concept and MPC products. In 2019, ARPA was named the Top 10 most innovative blockchain companies in China by China Enterprise News and China Software Industry Association.
For more information about ARPA or to join our team, please contact us at firstname.lastname@example.org.
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