Optimizing Homomorphic Encryption Parameters for Arbitrary Applications

Homomorphic encryption is a powerful privacy-preserving technology that is notoriously difficult to configure, even for experts. In this article, we outline methodologies for determining optimal cryptographic parameters for any arbitrary application. We provide guidelines for both leveled and fully homomorphic encryption, and demonstrate the presented strategies with the BGV cryptosystem

Privacy-Preserving IP Verification

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 \u27zero-knowledge proofs\u27 to enable 3PIP vendors to convince system integrators about various functional properties of a circuit (e.g., area, power, 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

SoK: New Insights into Fully Homomorphic Encryption Libraries via Standardized Benchmarks

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)

zk-Sherlock: Exposing Hardware Trojans in Zero-Knowledge

As integrated circuit (IC) design and manufacturing have become highly globalized, hardware security risks become more prominent as malicious parties can exploit multiple stages of the supply chain for profit. Two potential targets in this chain are third-party intellectual property (3PIP) vendors and their customers. Untrusted parties can insert hardware Trojans into 3PIP circuit designs that can both alter device functionalities when triggered or create a side channel to leak sensitive information such as cryptographic keys. To mitigate this risk, the absence of Trojans in 3PIP designs should be verified before integration, imposing a major challenge for vendors who have to argue their IPs are safe to use, while also maintaining the privacy of their designs before ownership is transferred. To achieve this goal, in this work we employ modern cryptographic protocols for zero-knowledge proofs and enable 3PIP vendors prove an IP design is free of Trojan triggers without disclosing the corresponding netlist. Our approach uses a specialized circuit compiler that transforms arbitrary netlists into a zero-knowledge-friendly format, and introduces a versatile Trojan detection module that maintains the privacy of the actual netlist. We evaluate the effectiveness of our methodology using selected benchmarks

REDsec: Running Encrypted Discretized Neural Networks in Seconds

Machine learning as a service (MLaaS) has risen to become a prominent technology due to the large development time, amount of data, hardware costs, and level of expertise required to develop a machine learning model. However, privacy concerns prevent the adoption of MLaaS for applications with sensitive data. A promising privacy preserving solution is to use fully homomorphic encryption (FHE) to perform the ML computations. Recent advancements have lowered computational costs by several orders of magnitude, opening doors for secure practical applications to be developed. In this work we introduce the REDsec framework that optimizes FHE-based private machine learning inference by leveraging ternary neural networks. Such neural networks, whose weights are constrained to {-1,0,1}, have special properties that we exploit to operate efficiently in the homomorphic domain. REDsec introduces novel features, including a new data re-use scheme that enables bidirectional bridging between the integer and binary domains for the first time in FHE. This enables us to implement very efficient binary operations for multiplication and activations, as well as efficient integer domain additions. Our approach is complemented by a new GPU acceleration library, dubbed (RED)cuFHE, which supports both binary and integer operations on multiple GPUs. REDsec brings unique benefits by supporting user-defined models as input (bring-your-own-network), automation of plaintext training, and efficient evaluation of private inference leveraging TFHE. In our analysis, we perform inference experiments with the MNIST, CIFAR-10, and ImageNet datasets and report performance improvements compared to related works

Accelerated Encrypted Execution of General-Purpose Applications

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, 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 over 40x faster on an NVIDIA DGX A100 and 6x faster with a single A100 relative to a 256-threaded CPU baseline

Romeo: Conversion and Evaluation of HDL Designs in the Encrypted Domain

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\u2785 and ISCAS\u2789 suites

MPloC: Privacy-Preserving IP Verification using Logic Locking and Secure Multiparty Computation

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 MPloC 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. MPloC 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