SoK: Cryptographically Protected Database Search

Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. Since protected search was introduced in 2000, the area has grown rapidly; systems are offered by academia, start-ups, and established companies. However, there is no best protected search system or set of techniques. Design of such systems is a balancing act between security, functionality, performance, and usability. This challenge is made more difficult by ongoing database specialization, as some users will want the functionality of SQL, NoSQL, or NewSQL databases. This database evolution will continue, and the protected search community should be able to quickly provide functionality consistent with newly invented databases. At the same time, the community must accurately and clearly characterize the tradeoffs between different approaches. To address these challenges, we provide the following contributions: 1) An identification of the important primitive operations across database paradigms. We find there are a small number of base operations that can be used and combined to support a large number of database paradigms. 2) An evaluation of the current state of protected search systems in implementing these base operations. This evaluation describes the main approaches and tradeoffs for each base operation. Furthermore, it puts protected search in the context of unprotected search, identifying key gaps in functionality. 3) An analysis of attacks against protected search for different base queries. 4) A roadmap and tools for transforming a protected search system into a protected database, including an open-source performance evaluation platform and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac

Cut-and-Choose for Garbled RAM

Garbled RAM, introduced by Lu and Ostrovsky (Eurocrypt 2013), provides a novel method for secure computation on RAM (Random Access Machine) programs directly. It can be seen as a RAM analogue of Yao\u27s garbled circuits such that the computational complexity and communication complexity only grow with the running time of the RAM program, avoiding the inefficient process of first converting it into a circuit. It allows for executing multiple RAM programs on a persistent database, but is secure only against semi-honest adversaries. In this work we provide a cut-and-choose technique for garbled RAM. This gives the first constant-round two-party RAM computation protocol secure against malicious adversaries which allows for multiple RAM programs being executed on a persistent database. Our protocol makes black-box use of the one-way functions, and security of our construction is argued in the random oracle model

Adaptively Secure Garbling with Near Optimal Online Complexity

We construct an adaptively secure garbling scheme with an online communication complexity of $n+m+\mathsf{poly}(\log |C|, \sec)$ where $C: \{0,1\}^n \rightarrow \{0,1\}^{m}$ is the circuit being garbled, and $\sec$ is the security parameter. The security of our scheme can be based on (polynomial hardness of) the Computational Diffie-Hellman (CDH) assumption, or the Factoring assumption or the Learning with Errors assumption. This is nearly the best achievable in the standard model (i.e., without random oracles) as the online communication complexity must be larger than both $n$ and $m$. The online computational complexity of our scheme is $O(n+m)+\mathsf{poly}(\log |C|, \sec)$. Previously known standard model adaptively secure garbling schemes had asymptotically worse online cost or relied on exponentially hard computational assumptions

Practical Garbled RAM: GRAM with O(log⁡2n)O(\log^2 n) Overhead

Garbled RAM (GRAM) is a powerful technique introduced by Lu and Ostrovsky that equips Garbled Circuit (GC) with a sublinear cost RAM without adding rounds of interaction. While multiple GRAM constructions are known, none are suitable for practice, due to costs that have high constants and poor scaling. We present the first GRAM suitable for practice. For computational security parameter $\kappa$ and for a size-$n$ RAM that stores blocks of size $w = \Omega(\log^2 n)$ bits, our GRAM incurs amortized $O(w \cdot \log^2 n \cdot \kappa)$ communication and computation per access. We evaluate the concrete cost of our GRAM; our approach outperforms trivial linear-scan-based RAM for as few as $512$ $128$-bit elements

Tri-State Circuits: A Circuit Model that Captures RAM

We introduce tri-state circuits (TSCs). TSCs form a natural model of computation that, to our knowledge, has not been considered by theorists. The model captures a surprising combination of simplicity and power. TSCs are simple in that they allow only three wire values ($0,1,$ and undefined - $\mathcal{Z}$) and three types of fan-in two gates; they are powerful in that their statically placed gates fire (execute) eagerly as their inputs become defined, implying orders of execution that depend on input. This behavior is sufficient to efficiently evaluate RAM programs. We construct a TSC that emulates $T$ steps of any RAM program and that has only $O(T \cdot \log^3 T \cdot \log \log T)$ gates. Contrast this with the reduction from RAM to Boolean circuits, where the best approach scans all of memory on each access, incurring quadratic cost. We connect TSCs with cryptography by using them to improve Yao\u27s Garbled Circuit (GC) technique. TSCs capture the power of garbling far better than Boolean Circuits, offering a more expressive model of computation that leaves per-gate cost essentially unchanged. As an important application, we construct authenticated Garbled RAM (GRAM), enabling constant-round maliciously-secure 2PC of RAM programs. Let $\lambda$ denote the security parameter. We extend authenticated garbling to TSCs; by simply plugging in our TSC-based RAM, we obtain authenticated GRAM running at cost $O(T \cdot \log^3 T \cdot \log \log T \cdot \lambda)$, outperforming all prior work, including prior semi-honest GRAM. We also give semi-honest garbling of TSCs from a one-way function (OWF). This yields OWF-based GRAM at cost $O(T \cdot \log^3 T \cdot \log \log T \cdot \lambda)$, outperforming the best prior OWF-based GRAM by more than factor $\lambda$