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DNA edit distance (ED) measures the minimum number of single nucleotide insertions, substitutions, or deletions required to convert a DNA sequence into another. ED has broad applications in healthcare such as sequence alignment, genome assembly, functional annotation, and drug discovery. Privacy-preserving computation is essential in this context to protect sensitive genomic data. Nonetheless, the existing secure DNA edit distance solutions lack efficiency when handling large data sequences or resort to approximations and fail to accurately compute the metric. In this work, we introduce ScureED, a protocol that tackles these limitations, resulting in a significant performance enhancement of approximately 2-24 times compared to existing methods. Our protocol computes a secure ED between two genomes, each comprising 1,000 letters, in just a few seconds. The underlying technique of our protocol is a novel approach that transforms the established approximate matching technique (i.e., the Ukkonen algorithm) into exact matching, exploiting the inherent similarity in human DNA to achieve cost-effectiveness. Furthermore, we introduce various optimizations tailored for secure computation in scenarios with a limited input domain, such as DNA sequences composed solely of the four nucleotide letters. 

In crowd-sourced data aggregation over the Internet, participants share their data points with curators. However, a lack of strong privacy guarantees may discourage participation, which motivates the need for privacy-preserving aggregation protocols. Moreover, existing solutions remain limited with respect to public auditing without revealing the participants’ data. In realistic applications, however, there is an increasing need for public verifiability (i.e., verifying the protocol correctness) while preserving the privacy of the participants’ inputs, since the participants do not always trust the data curators. At the same time, while publicly distributed ledgers may provide public auditing, these schemes are not designed to protect sensitive information. In this work, we introduce two protocols, dubbed Masquerade and zk-Masquerade, for computing private statistics, such as sum, average, and histograms, without revealing anything about participants’ data. We propose a tailored multiplicative commitment scheme to ensure the integrity of data aggregations and publish all the participants’ commitments on a ledger to provide public verifiability. zk-Masquerade detects malicious participants who attempt to poison the aggregation results by adopting two zero-knowledge proof protocols that ensure the validity of shared data points before being aggregated and enable a broad range of numerical and categorical studies. In our experiments, we use homomorphic ciphertexts and commitments for a variable number of participants and evaluate the runtime and the communication cost of our protocols. 

As cloud computing continues to gain widespread adoption, safeguarding the confidentiality of data entrusted to third-party cloud service providers becomes a critical concern. While traditional encryption methods offer protection for data at rest and in transit, they fall short when it comes to where it matters the most, i.e., during data processing. To address this limitation, we present HELM, a framework for privacy-preserving data processing using homomorphic encryption. HELM automatically transforms arbitrary programs expressed in a Hardware Description Language (HDL), such as Verilog, into equivalent homomorphic circuits, which can then be efficiently evaluated using encrypted inputs. HELM features three modes of encrypted evaluation: a) a gate mode that consists of Boolean gates, b) a small-precision lookup table mode which significantly reduces the size of the circuit by combining multiple gates into lookup tables, and c) a high-precision lookup table mode tuned for multi-bit arithmetic evaluations. Finally, HELM introduces a scheduler that leverages the parallelism inherent in arithmetic and Boolean circuits to efficiently evaluate encrypted programs. We evaluate HELM with the ISCAS'85 and ISCAS'89 benchmark suites, as well as real-world applications such as image filtering and neural network inference. In our experimental results, we report that HELM can outperform prior works by up to 65x. 

Homomorphic encryption can address key privacy challenges in cloud-based outsourcing by enabling potentially untrusted servers to perform meaningful computation directly on encrypted data. While most homomorphic encryption schemes offer addition and multiplication over ciphertexts natively, any non-linear functions must be implemented as costly polynomial approximations due to this restricted computational model. Nevertheless, the CGGI cryptosystem is capable of performing arbitrary univariate functions over ciphertexts in the form of lookup tables through the use of programmable bootstrapping. While promising, this procedure can quickly become costly when high degrees of precision are required. To address this challenge, we propose Ripple: a framework that introduces different approximation methodologies based on discrete wavelet transforms (DWT) to decrease the number of entries in homomorphic lookup tables while maintaining high accuracy. Our empirical evaluations demonstrate significant error reduction compared to plain quantization methods across multiple non-linear functions. Notably, Ripple improves runtime performance for realistic applications, such as logistic regression and Euclidean distance. 

Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&P'21) and compare in terms of monetary cost for different input sizes. 

Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet’s capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers SIMON and SPECK, as well as logistic regression (LR) inference and matrix multiplication. 

Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators.