With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvements. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. Our implementation on reconfigurable hardware demonstrates 164-268× performance improvement for a wide range of FHE parameters.

As the amount of data generated by the Internet of the Things (IoT) devices keeps increasing, many applications need to offload computation to the cloud. However, it often entails risks due to security and privacy issues. Encryption and decryption methods add to an already significant computational burden. In this paper, we propose a novel framework, called SecureHD, which provides a secure learning solution based on the idea of high-dimensional (HD) computing. We encode original data into secure, high-dimensional vectors. The training is performed with the encoded vectors. Thus, applications can send their data to the cloud with no security concerns, while the cloud can perform the offloaded tasks without additional decryption steps. In particular, we propose a novel HD-based classification algorithm which is suitable to handle a large amount of data that the cloud typically processes. In addition, we also show how SecureHD can recover the encoded data in a lossless manner. In our evaluation, we show that the proposed SecureHD framework can perform the encoding and decoding tasks 145.6× and 6.8× faster than a state-of-the-art encryption/decryption library running on the contemporary CPU. In addition, our learning method achieves high accuracy of 95% on average for diverse practical classification tasks including cloud-scale datasets.

Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful "fuzzy" cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics --- e.g., multiple fingerprints --- of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints --- fingerprints of ten fingers --- called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database.

We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applications using standard high-level programming languages (e.g., C) and compile them using off-the-shelf ARM compilers, e.g., gcc-arm. The main enabler of this framework is the introduction of SkipGate, an algorithm that dynamically omits the communication and encryption cost of a gate when its output is independent of the private data. SkipGate greatly enhances the performance of ARM2GC by omitting costs of the gates associated with the instructions of the compiled binary, which is known by both parties involved in the computation. Our evaluation on benchmark functions demonstrates that ARM2GC outperforms the prior best solution by 156×.

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbbZ _2^l $ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.

This paper presents DeepSecure, the an scalable and provably secure Deep Learning (DL) framework that is built upon automated design, efficient logic synthesis, and optimization methodologies. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves up to 58-fold higher throughput per sample compared with the best prior solution. In addition to the optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of DL. Our extensive evaluations demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology.

We provide a systemization of knowledge of the recent progress made in addressing the crucial problem of deep learning on encrypted data. The problem is important due to the prevalence of deep learning models across various applications, and privacy concerns over the exposure of deep learning IP and user's data. Our focus is on provably secure methodologies that rely on cryptographic primitives and not trusted third parties/platforms. Computational intensity of the learning models, together with the complexity of realization of the cryptography algorithms hinder the practical implementation a challenge. We provide a summary of the state-of-the-art, comparison of the existing solutions, as well as future challenges and opportunities.

In this article, we present SHAIP, a secure Hamming distance–based mutual authentication protocol. It allows an unlimited number of authentications by employing an intrinsic Physical Unclonable Function (PUF). PUFs are being increasingly employed for remote authentication of devices. Most of these devices have limited resources. Therefore, the intrinsic PUFs are most suitable for this task as they can be built with little or no modification to the underlying hardware platform. One major drawback of the current authentication schemes is that they expose the PUF response. This makes the intrinsic PUFs, which have a limited number of challenge-response pairs, unusable after a certain number of authentication sessions. Moreover, these schemes are one way in the sense that they only allow one party, the prover, to authenticate herself to the verifier. We propose a symmetric mutual authentication scheme based on secure (privacy-preserving) computation of the Hamming distance between the PUF response from the remote device and reference response stored at the verifier end. This allows both parties to authenticate each other without revealing their respective sets of inputs. We show that our scheme is effective with all state-of-the-art intrinsic PUFs. The proposed scheme is lightweight and does not require any modification to the underlying hardware.

We propose PriSearch, a provably secure methodology for two-party string search. The scenario involves two parties, Alice (holding a query string) and Bob (holding a text), who wish to perform a string search while keeping both the query and the text private without relying on any third party. Such privacy-preserving string search avoids any data leakage when handling sensitive information, e.g., genomic data. PriSearch provides an efficient solution where two parties only need to interact for a constant number of rounds independent of the query and text size. Our approach is based on the provably secure Yao's Garbled Circuit (GC) protocol that requires the string search algorithm to be described as a Boolean circuit. We leverage logic synthesis tools to generate an optimized Boolean circuit for PriSearch such that it incurs the minimum communication/computation cost. We achieve approximately 2x and 140x performance improvements compared to the best prior non-GC and GC-based solutions, respectively.

Abstract The Stable Matching (SM) algorithm has been deployed in many real-world scenarios including the National Residency Matching Program (NRMP) and financial applications such as matching of suppliers and consumers in capital markets. Since these applications typically involve highly sensitive information such as the underlying preference lists, their current implementations rely on trusted third parties. This paper introduces the first provably secure and scalable implementation of SM based on Yao’s garbled circuit protocol and Oblivious RAM (ORAM). Our scheme can securely compute a stable match for 8k pairs four orders of magnitude faster than the previously best known method. We achieve this by introducing a compact and efficient sub-linear size circuit. We even further decrease the computation cost by three orders of magnitude by proposing a novel technique to avoid unnecessary iterations in the SM algorithm. We evaluate our implementation for several problem sizes and plan to publish it as open-source.

We introduce CAMsure, the first realization of secure Content Addressable Memory (CAM) in the context of approximate search using near-neighbor algorithms. CAMsure provides a lightweight solution for practical secure (approximate) search with a minimal drop in the accuracy of the search results. CAM has traditionally been used as a hardware search engine that explores the entire memory in a single clock cycle. However, there has been little attention to the security of the data stored in CAM. Our approach stores distance-preserving hash embeddings within CAM to ensure data privacy. The hashing method provides data confidentiality while preserving similarity in the sense that a high resemblance in the data domain is translated to a small Hamming distance in the hash domain. Consequently, the objective of near-neighbor search is converted to approximate lookup table search which is compatible with the realizations of emerging content addressable memories. Our methodology delivers on average two orders of magnitude faster response time compared to RAM-based solutions that preserve the privacy of data owners.