In private information retrievals (PIRs), a client retrieves an entry from a database without letting the database server know which entry is retrieved. Recently, hint-based PIRs were proposed, where the client receives some hints about the database in the setup phase before the actual query begins. As an advantage, these PIRs achieve sublinear database server online computation. On the other hand, the client needs to preprocess the hints with the database server, which can be costly. Moreover, the client needs to store some amount of hints, even if the client plans to query only once or a few times. In this paper, we are the first to propose a hint-based PIR with commodity servers, which we call CHOO-PIR. The purpose of the commodity servers is to manage the hints for the client. In order to preserve the privacy of the client against the commodity servers, we apply fully homomorphic encryption (FHE) or secret sharing (SS) schemes. Our FHE-based scheme achieves both client computational cost of $\widetilde{O}(1)$ and database server online computational cost of $\widetilde{O}(\sqrt{n})$, where n is the database size. While the FHE-based scheme requires public-key cryptography, we can avoid such operations in the SS-based scheme, with a trade-off of client computational cost and online communication cost.

Reputation systems are helpful for our decision making when we have to deal with unfamiliar service providers. However, some of these systems do not preserve privacy of the users, e.g., rating from an individual is obviously shown. Therefore, users may provide dishonest feedback due to the fear of retaliation. Although, some privacy-preserving reputation systems are proposed in the literature, most of them cannot withstand some specific attacks. These attacks include "dishonest queries to similar sets of users" and "collusion between the dishonest querier and all-but-one users". As a result, rating from an individual may be leaked.As the contribution of this work, we propose a novel privacy-preserving reputation system that addresses the aforementioned attacks. The idea is that, the querier firstly specifies a set of users, then the average rating is computed from a random subset of that set. Since the members of the random subset are not known to anyone, the attacks become difficult. We construct our scheme based on a secret sharing technique, specifically ABY3 framework (Mohassel and Rindal, CCS’18) that supports conversion between different data representations. Correctness, security, privacy, and complexity are then discussed, and possible improvements are also suggested. The experiments show that the accuracy of our scheme is similar to the case where privacy is not preserved.