As the field of genomics continues to expand and more sequencing data is gathered, genome analysis becomes increasingly relevant for many users. For example, a common scenario involves users trying to determine if collected DNA samples are similar to DNA sequences hosted in a remote repository, such as checking for disease mutations in patients with monogenic disorders. Nevertheless, end users may be reluctant to upload sensitive DNA sequences, which might reveal private biological information, while the owners of remote genomics repositories are unwilling to share their databases. To address this challenge, we propose a novel Bloom filter approach based on fully homomorphic encryption to preserve the privacy of the genomic data and enable queries directly on ciphertexts. We utilize the Circuit Bootstrapping and CMUX functionality in TFHE to implement the homomorphic indexing required in Bloom filters. Our EDA-inspired techniques can privately determine if a client's DNA sequence exists in a database. We validate our approach across various database sizes and compare directly with related works. We observe up to 4 orders of magnitude faster server evaluation times and many orders of magnitude smaller communication overhead for our methodology.

Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation. FHE algorithms can perform unlimited arithmetic computations directly on encrypted data without decrypting it. Thus, even when processed by untrusted systems, confidential data is never exposed. In this work, we develop new techniques for accelerated encrypted execution and demonstrate the significant performance advantages of our approach. Our current focus is the Fully Homomorphic Encryption over the Torus (CGGI) scheme, which is a current state-of-the-art method for evaluating arbitrary functions in the encrypted domain. CGGI represents a computation as a graph of homomorphic logic gates and each individual bit of the plaintext is transformed into a polynomial in the encrypted domain. Arithmetic on such data becomes very expensive: operations on bits become operations on entire polynomials. Therefore, evaluating even relatively simple nonlinear functions with the CGGI cryptosystem, such as a sigmoid, can take thousands of seconds on a single CPU thread. Using our novel framework for end-to-end accelerated encrypted execution called ArctyrEX, developers with no knowledge of complex FHE libraries can simply describe their computation as a C program that is evaluated 18x faster on average relative to the GPU-accelerated Concrete library for multiplication-intensive benchmarks.

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$65\times $.

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure and use, even for experts. The key difficulties include restrictive programming models of homomorphic schemes and choosing suitable parameters for an application. In this tutorial, we outline methodologies to solve these issues and allow for conversion of any application to the encrypted domain using both leveled and fully homomorphic encryption. The first approach, called Walrus, is suitable for arithmetic-intensive applications with limited depth and applications with high throughput requirements. Walrus provides an intuitive programming interface and handles parameterization automatically by analyzing the application and gathering statistics such as homomorphic noise growth to derive a parameter set tuned specifically for the application. We provide an in-depth example of this approach in the form of a neural network inference as well as guidelines for using Walrus effectively. Conversely, the second approach (HELM) takes existing HDL designs and converts them to the encrypted domain for secure outsourcing on powerful cloud servers. Unlike Walrus, HELM supports FHE backends and is well-suited for complex applications. At a high level, HELM consumes netlists and is capable of performing logic gate operations homomorphically on encryptions of individual bits. HELM incorporates both CPU and GPU acceleration by taking advantage of the inherent parallelism provided by Boolean circuits. As a case study, we walk through the process of taking an off-the-shelf HDL design in the form of AES-128 decryption and running it in the encrypted domain with HELM.

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 ciphersSimonandSpeck, as well as logistic regression (LR) inference and matrix multiplication.

The global supply chain involves multiple independent entities, and potential adversaries can exploit different attack vectors to steal proprietary designs and information. As a result, intellectual property (IP) owners and consumers have reasons to keep their designs private. Without a trusted third party, this mutual mistrust can lead to a deadlock where IP owners are unwilling to disclose their IP core before a financial agreement is reached, while consumers need assurance that the proprietary design will meet their integration needs without compromising the confidentiality of their test vectors. To address this challenge, we introduce an efficient framework called$\text{MP}\ell_{\circ}\mathrm{C}$that resolves this deadlock by allowing owners and consumers to jointly evaluate the target design with consumer-supplied test vectors while preserving the privacy of both the IP core and the inputs.$\mathbf{MP}\ell\circ \mathbf{C}$is the first work that combines secure multiparty computation (MPC) and logic-locking techniques to accomplish these goals. Our approach supports both semi-honest and malicious security models to allow users to balance stronger security guarantees with performance. We compare our approach to existing state-of-the-art works that utilize homomorphic encryption across several benchmarks and report runtime improvements of more than two orders of magnitude.

Fully homomorphic encryption (FHE) enables arbitrary computation on encrypted data, allowing users to upload ciphertexts to cloud servers for computation while mitigating privacy risks. Many cryptographic schemes fall under the umbrella of FHE, and each scheme has several open-source implementations with its own strengths and weaknesses. Nevertheless, developers have no straightforward way to choose which FHE scheme and implementation is best suited for their application needs, especially considering that each scheme offers different security, performance, and usability guarantees. To allow programmers to effectively utilize the power of FHE, we employ a series of benchmarks called the Terminator 2 Benchmark Suite and present new insights gained from running these algorithms with a variety of FHE back - ends. Contrary to generic benchmarks that do not take into consideration the inherent challenges of encrypted computation, our methodology is tailored to the secure computational primitives of each target FHE implementation. To ensure fair comparisons, we developed a versatile compiler(called T2) that converts arbitrary benchmarks written in a domain - specific language into identical encrypted programs running on different popular FHE libraries as a backend.Our analysis exposes for the first time the advantages and disadvantages of each FHE library as well as the types of applications most suited for each computational domain(i.e., binary, integer, and floating - point).

The rapid growth of the globalized integrated circuit (IC) supply chain has drawn the attention of numerous malicious actors that try to exploit it for profit. One of the most prominent targets of such parties is the third-party intellectual property (3PIP) vendors and their circuit designs. With the increasing number of transactions between vendors and system integrators, the threat of IP reuse and piracy has become a significant consideration for the IC industry. What is more, the correctness of 3PIP designs should be verified before integration, imposing another challenge for 3PIP vendors since they have to prove the functionality of their designs to system integrators while protecting the privacy of the circuit implementations. To eliminate this deadlock, we utilize the cryptographic technique of “zero-knowledge proofs” to enable 3PIP vendors to convince system integrators about various functional properties of a circuit (e.g., area, power, and frequency) without disclosing its netlist (i.e., in zero-knowledge). Our approach comprises a circuit compiler that transforms arbitrary netlists into a zero knowledge-friendly format and a library of modules that provide cryptographic guarantees for various properties of the netlist while hiding the actual gates. We evaluate our method using combinational and sequential circuits from the ISCAS and ITC benchmark suites.

As cloud computing becomes increasingly ubiquitous, protecting the confidentiality of data outsourced to third parties becomes a priority. While encryption is a natural solution to this problem, traditional algorithms may only protect data at rest and in transit, but do not support encrypted processing. In this work we introduce ROMEO, which enables easy-to-use privacy-preserving processing of data in the cloud using homomorphic encryption. ROMEO automatically converts arbitrary programs expressed in Verilog HDL into equivalent homomorphic circuits that are evaluated using encrypted inputs. For our experiments, we employ cryptographic circuits, such as AES, and benchmarks from the ISCAS'85 and ISCAS'89 suites.

As the Internet of Things (IoT) continues to expand into every facet of our daily lives, security researchers have warned of its myriad security risks. While denial-of-service attacks and privacy violations have been at the forefront of research, covert channel communications remain an important concern. Utilizing a Bluetooth controlled light bulb, we demonstrate three separate covert channels, consisting of current utilization, luminosity and hue. To study the effectiveness of these channels, we implement exfiltration attacks using standard off-the-shelf smart bulbs and RGB LEDs at ranges of up to 160 feet. We analyze the identified channels for throughput, generality and stealthiness, and report transmission speeds of up to 832 bps.