This paper presents Aegean, a new approach that allows fault-tolerant replication to be implemented beyond the confines of the client-server model. In today's computing, where services are rarely standalone, traditional replication protocols such as Primary-Backup, Paxos, and PBFT are not directly applicable, as they were designed for the client-server model. When services interact, these protocols run into a number of problems, affecting both correctness and performance. In this paper, we rethink the design of replication protocols in the presence of interactions between services and introduce new techniques that accommodate such interactions safely and efficiently. Our evaluation shows that a prototype implementation of Aegean not only ensures correctness in the presence of service interactions, but can further improve throughput by an order of magnitude.

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.