Computing Privacy-Preserving Edit Distance and Smith-Waterman Problems on the GPU Architecture

This paper presents privacy-preserving, parallel computing algorithms on a graphic processing unit (GPU) architecture to solve the Edit-Distance (ED) and the Smith-Waterman (SW) problems. The ED and SW problems are formulated into dynamic programming (DP) computing problems, which are solved using the Secure Function Evaluation (SFE) to meet privacy protection requirements, based on the semi-honest security model. Major parallelization techniques include mapping of variables to support collision-free parallel memory access, scheduling and mapping of gate garblers on GPU devices to maximize GPU device utilization, and latency minimization of context switch for computing steps in the DP matrix. A pipelined GPU-CPU interface is developed to mask latency of CPU housekeeping components. The new solutions were tested on a Xeon E5504 at 2GHz plus a GTX-680 GPU (as generator), connecting an i7-3770K at 3.5GHz plus a GTX-680 GPU (as evaluator) via local Internet. A 5000×5000 8-bit alphabet ED problem requires roughly 1.88 billion non-free gates, and the running time of around 26 minutes (roughly 1.209×106 gate/second). A 60×60 SW problem is computed in around 16.79 seconds. Compared to the state of art performance [5], we achieved the acceleration factor of 12.5× for the ED problem, and 24.7× for the SW problem

FleXOR: Flexible Garbling for XOR Gates That Beats Free-XOR

Most implementations of Yao\u27s garbled circuit approach for 2-party secure computation use the {\em free-XOR} optimization of Kolesnikov \& Schneider (ICALP 2008). We introduce an alternative technique called {\em flexible-XOR} (fleXOR) that generalizes free-XOR and offers several advantages. First, fleXOR can be instantiated under a weaker hardness assumption on the underlying cipher/hash function (related-key security only, compared to related-key and circular security required for free-XOR) while maintaining most of the performance improvements that free-XOR offers. Alternatively, even though XOR gates are not always ``free\u27\u27 in our approach, we show that the other (non-XOR) gates can be optimized more heavily than what is possible when using free-XOR. For many circuits of cryptographic interest, this can yield a significantly (over 30\%) smaller garbled circuit than any other known techniques (including free-XOR) or their combinations

An efficient framework for privacy-preserving computations on encrypted IoT data

There are two fundamental expectations from Cloud-IoT applications using sensitive and personal data: data utility and user privacy. With the complex nature of cloud-IoT ecosystem, there is a growing concern about data utility at the cost of privacy. While the current state-of-the-art encryption schemes protect users’ privacy, they preclude meaningful computations on encrypted data. Thus, the question remains “how to help IoT device users benefit from cloud computing without compromising data confidentiality and user privacy”? Cloud service providers (CSP) can leverage Fully homomorphic encryption (FHE) schemes to deliver privacy-preserving services. However, there are limitations in directly adopting FHE-based solutions for real-world Cloud-IoT applications. Thus, to foster real-world adoption of FHE-based solutions, we propose a framework called Proxy re-ciphering as a service. It leverages existing schemes such as distributed proxy servers, threshold secret sharing, chameleon hash function and FHE to tailor a practical solution that enables long-term privacy-preserving cloud computations for IoT ecosystem. We also encourage CSPs to store minimal yet adequate information from processing the raw IoT device data. Furthermore, we explore a way for IoT devices to refresh their device keys after a key-compromise. To evaluate the framework, we first develop a testbed and measure the latencies with real-world ECG records from TELE ECG Database. We observe that i) although the distributed framework introduces computation and communication latencies, the security gains outweighs the latencies, ii) the throughput of the servers providing re-ciphering service can be greatly increased with pre-processing iii) with a key refresh scheme we can limit the upper bound on the attack window post a key-compromise. Finally, we analyze the security properties against major threats faced by Cloud-IoT ecosystem. We infer that Proxy re-ciphering as a service is a practical, secure, scalable and an easy-to-adopt framework for long-term privacy-preserving cloud computations for encrypted IoT data

Efficient Private Matching for Private Databases

Private matching (PM) is a key cryptographic primitive in secure computation that allows several parties to jointly compute some functions depending on their private inputs. Indeed, this primitive has many practical applications. For instance, in online advertising, two companies may wish to find their common customers for a joint marketing campaign. In this scenario, privacy is of utmost importance and it is imperative to ensure that neither company can learn more than their own data and the results of the match. In recent years, secure computation in general and PM in particular has attracted considerable attention from the research community, partly due to the rise of Big Data along with the ever-increasing privacy concerns. This thesis describes three secure private set intersection (PSI) protocols, one private set union (PSU) construction, and one pattern matching scheme. PM can be considered as a main building block in all these protocols. To securely compute the intersection of two sets of size $2^{20}$ our proposed protocols require only 3 seconds which is $4\times$ faster than the previous best protocol. In the multi-party setting, we provide the first implementation that takes only 72 seconds to compute PSI for 5 parties with data-sets of $2^{20}$ items each. For private set union (PSU), our protocol improves prior work by a factor up to $7,600 \times$ for large instances. In addition, our wild-card pattern matching (WPM) protocol shows over two orders of magnitude faster than the state-of-the-art scheme