Privacy-enhanced computation enables the processing of encrypted data without exposing underlying sensitive information. Such technologies are extremely promising for the advancement of data privacy, as they remove plaintexts from the attackers' reach. However, each privacy technology provides varying degrees of computational capabilities and performance overheads, creating challenges for adoption. For example, some publicly available homomorphic encryption schemes are limited in expressiveness or cannot support deep computation without incurring significant overheads. This diversity warrants a benchmark suite that can adequately assess capability and performance while supporting a variety of privacy-enhanced software architectures. We propose VIP-Bench, a benchmark suite designed with varying operational complexity and computational depth to evaluate competing privacy frameworks fairly and directly. VIP-Bench defines a forward-looking privacy-enhanced computation model and then develops under that model an array of privacy-focused benchmarks. The benchmark set is designed to flexibly cover the whole range of expected computational power and capability, enabling VIP-Bench to evaluate the privacy-enhanced computation capabilities of both today and tomorrow.