Dimitris Mouris

Transport Layer Security (TLS) allows clients to establish secure and private channels with servers. DECO (CCS'20) and follow-up works proposed protocols that enable proving the provenance of a TLS response, i.e., that a payload came from a particular server, without needing server-side modifications. Unfortunately, these works are limited to proving Boolean statements over the payload (e.g., age ≥ 18) and cannot combine payloads from multiple clients. We introduce TLShare, a framework that extracts authenticated data from a TLS connection and imports it into secure multiparty computation (MPC) or fully homomorphic encryption (FHE), without requiring server-side changes or exposing client credentials. Unlike prior work, TLShare allows the payload itself, not just a predicate about it, to serve as private input to secure downstream computation. TLShare supports combining verifiable inputs across multiple clients and servers, enabling new applications such as privacy-preserving financial risk assessment and collaborative analytics. We design three protocols for TLShare: one for MPC using verifiable secret sharing, and two for FHE using interactive and non-interactive zero-knowledge proofs, each ensuring input authenticity, integrity, and end-to-end privacy. We evaluate all three protocols of TLShare over both LAN and WAN settings, comparing their trade-offs and demonstrating their practicality.

Private histogram computation is a fundamental building block for many data analytics tasks, enabling frequency analysis without revealing individual inputs. Existing protocols achieving robustness against malicious clients and servers typically require three servers with limited adversarial tolerance, restricting practicality. In this work, we present Paras, the first two-server protocol for private histogram computation that achieves robustness against collusion between a malicious server and arbitrarily many malicious clients. Paras builds upon distributed point function-based approaches and introduces novel consistency checks leveraging vector oblivious linear evaluation (VOLE) to enforce both input correctness and output integrity. To realize these checks, we design two new cryptographic primitives: (1) aBV, an authenticated bit verification protocol that ensures VOLE committed shares correspond to valid bits, and (2) adIPA, an authenticated double inner product argument that enables secure consistency checks across two different VOLE sessions. These primitives may be of independent interest for other secure computation tasks. We show that Paras is highly efficient and scalable: clients incur minimal cost independent of domain size, while servers achieve low per-client runtime, communication, and storage even at scale. For example, with 8192 clients over a domain of 128 inputs, each server requires only 14 ms runtime and 24 KB communication per client.

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.

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 multibit 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 65×.

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.

Insight into user experience and behavior is critical to the success of large software systems and web services. Gaining such insights, while preserving user privacy, is a significant challenge. Recent advancements in multi-party computation have made it practical to securely compute aggregates over secret shared data. Two such protocols have emerged as candidates for standardization at IETF: Prio (NSDI 2017) for general-purpose statistics; and Poplar (IEEE S&P 2021) for heavy hitters, where the goal is to compute the most popular inputs held by users without learning the inputs themselves. While each of these protocols is well-suited to certain applications, there remain a number of use cases identified by IETF for which neither Prio nor Poplar is practical. We introduce Mastic, a protocol for the following functionality: each of a large number of clients holds an input (e.g., a URL) and its corresponding weight (e.g., page load time); for a given candidate input (or prefix), a small number of non-colluding servers wish to securely aggregate the weights of clients that hold that input (or some input with that prefix), without learning the weights or which client holds which input. This functionality makes two new classes of applications possible. The first is a natural generalization of heavy hitters we call weighted heavy-hitters. The second is an enhancement of Prio-style metrics we call attribute-based metrics in which aggregates are grouped by hierarchical user attributes (e.g., their geographic location or software version). We demonstrate Mastic's practicality for these applications with a real-world example of each. We also compare our protocol with Prio and Poplar on a wide area network. Overall, we report over one order of magnitude performance improvement over Poplar for plain heavy-hitters and 1.5-2x improvement over Prio for attribute-based metrics.

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.