Pattern matching methods are essential in various applications where users must disclose highly sensitive information. Among these applications are genomic data analysis, financial records inspection, and intrusion detection processes, all of which necessitate robust privacy protection mechanisms. Balancing the imperative of protecting the confidentiality of analyzed data with the need for efficient pattern matching presents a significant challenge.

In this paper, we propose an efficient post-quantum secure construction that enables arbitrary pattern matching over encrypted data while ensuring the confidentiality of the data to be analyzed. In addition, we address scenarios where a malicious data sender, intended to send an encrypted content for pattern detection analysis, has the ability to modify the encrypted content. We adapt the data fragmentation technique to handle such a malicious sender. Our construction makes use of a well-suited Homomorphic Encryption packing method in the context of fragmented streams and combines homomorphic operations in a leveled mode (i.e. without bootstrapping) to obtain a very efficient pattern matching detection process.

In contrast to the most efficient state-of-the-art scheme, our construction achieves a significant reduction in the time required for encryption, decryption, and pattern matching on encrypted data. Specifically, our approach decreases the time by factors of $1850$, $10^6$, and $245$, respectively, for matching a single pattern, and by factors of $115$, $10^5$, and $12$, respectively, for matching $2^{10}$ patterns.

Fully Homomorphic Encryption (FHE) is a powerful tool to achieve non-interactive privacy preserving protocols with optimal computation/communication complexity. However, the main disadvantage is that the actual communication cost (bandwidth) is high due to the large size of FHE ciphertexts. As a solution, a technique called transciphering (also known as Hybrid Homomorphic Encryption) was introduced to achieve almost optimal bandwidth for such protocols. However, all existing works require clients to fix a precision for the messages or a mathematical structure for the message space beforehand. It results in unwanted constraints on the plaintext size or underlying structure of FHE based applications.

In this article, we introduce a new approach for transciphering which does not require fixed message precision decided by the client, for the first time. In more detail, a client uses any kind of FHE-friendly symmetric cipher for $\{0,1\}$ to send its input data encrypted bit-by-bit, then the server can choose a precision $p$ depending on the application and homomorphically transforms the encrypted bits into FHE ciphertexts encrypting integers in $\mathbb{Z}_p$. To illustrate our new technique, we evaluate a transciphering using FiLIP cipher and adapt the most practical homomorphic evaluation technique [CCS'22] to keep the practical latency. As a result, our proof-of-concept implementation for $p$ from $2^2$ to $2^8$ takes only from $13$ ms to $137$ ms.

Fully Homomorphic Encryption (FHE) is a prevalent cryptographic primitive that allows for computation on encrypted data. In various cryptographic protocols, this enables outsourcing computation to a third party while retaining the privacy of the inputs to the computation. However, these schemes make an honest-but-curious assumption about the adversary. Previous work has tried to remove this assumption by combining FHE with Verifiable Computation (VC). Recent work has increased the flexibility of this approach by introducing integrity checks for homomorphic computations over rings. However, efficient FHE for circuits of large multiplicative depth also requires non-ring computations called maintenance operations, i.e. modswitching and keyswitching, which cannot be efficiently verified by existing constructions. We propose the first efficiently verifiable FHE scheme that allows for arbitrary depth homomorphic circuits by utilizing the double-CRT representation in which FHE schemes are typically computed, and using lattice-based SNARKs to prove components of this computation separately, including the maintenance operations. Therefore, our construction can theoretically handle bootstrapping operations. We also present the first implementation of a verifiable computation on encrypted data for a computation that contains multiple ciphertext-ciphertext multiplications. Concretely, we verify the homomorphic computation of an approximate neural network containing three layers and >100 ciphertexts in less than 1 second while maintaining reasonable prover costs.