We present several optimizations to SPHINCS, a stateless hash-based signature scheme proposed by Bernstein et al. in 2015: PORS, a more secure variant of the HORS few-time signature scheme used in SPHINCS; secret key caching, to speed-up signing and reduce signature size; batch signing, to amortize signature time and reduce signature size when signing multiple messages at once; mask-less constructions to reduce the key size and simplify the scheme; and Octopus, a technique to eliminate redundancies from authentication paths in Merkle trees. Based on a refined analysis of the subset resilience problem, we show that SPHINCS' parameters can be modified to reduce the signature size while retaining a similar security level and computation time. We then propose Gravity-SPHINCS, our variant of SPHINCS embodying the aforementioned tricks. Gravity-SPHINCS has shorter keys (32 and 64 bytes instead of ≈1 KB), shorter signatures (≈30 KB instead of 41 KB), and faster signing and verification for a same security level as SPHINCS.

PDF has become a de facto standard for exchanging electronic documents, for visualization as well as for printing. However, it has also become a common delivery channel for malware, and previous work has highlighted features that lead to security issues. In our work, we focus on the structure of the format, independently from specific features. By methodically testing PDF readers against hand-crafted files, we show that the interpretation of PDF files at the structural level may cause some form of denial of service, or be ambiguous and lead to rendering inconsistencies among readers. We then propose a pragmatic solution by restricting the syntax to avoid common errors, and propose a formal grammar for it. We explain how data consistency can be validated at a finer-grained level using a dedicated type checker. Finally, we assess this approach on a set of real-world files and show that our proposals are realistic.

In recent years, ride-hailing services (RHSs) have become increasingly popular, serving millions of users per day. Such systems, however, raise significant privacy concerns, because service providers are able to track the precise mobility patterns of all riders and drivers. In this paper, we propose ORide (Oblivious Ride), a privacy-preserving RHS based on somewhat-homomorphic encryption with optimizations such as ciphertext packing and transformed processing. With ORide, a service provider can match riders and drivers without learning their identities or location information. ORide offers riders with fairly large anonymity sets (e.g., several thousands), even in sparsely populated areas. In addition, ORide supports key RHS features such as easy payment, reputation scores, accountability, and retrieval of lost items. Using real data-sets that consist of millions of rides, we show that the computational and network overhead introduced by ORide is acceptable. For example, ORide adds only several milliseconds to ride-hailing operations, and the extra driving distance for a driver is less than 0.5 km in more than 75% of the cases evaluated. In short, we show that a RHS can offer strong privacy guarantees to both riders and drivers while maintaining the convenience of its services.