As billions of people rely on end-to-end encrypted messaging, the exposure of metadata, such as communication timing and participant relationships, continues to deanonymize users. Asynchronous metadata-hiding solutions with strong cryptographic guarantees have historically been bottlenecked by quadratic$O(N^{2})$server computation in the number of users$N$due to reliance on private information retrieval (PIR). We present Myco, a metadata-private messaging system that preserves strong cryptographic guarantees while achieving$O(N\log^{2}N)$efficiency. To achieve this, we depart from PIR and instead introduce an oblivious data structure through which senders and receivers privately communicate. To unlink reads and writes, we instantiate Myco in an asymmetric two-server distributed-trust model where clients write messages to one server tasked with obliviously transmitting these messages to another server, from which clients read. Myco achieves throughput improvements of up to 302x over multi-server and 2,219x over single-server state-of-the-art systems based on PIR.

We build a library SecFloat for secure 2-party computation (2PC) of 32-bit single-precision floating-point operations and math functions. The existing functionalities used in cryptographic works are imprecise and the precise functionalities used in standard libraries are not crypto-friendly, i.e., they use operations that are cheap on CPUs but have exorbitant cost in 2PC. SecFloat bridges this gap with its novel crypto-friendly precise functionalities. Compared to the prior cryptographic libraries, SecFloat is up to six orders of magnitude more precise and up to two orders of magnitude more efficient. Furthermore, against a precise 2PC baseline, SecFloat is three orders of magnitude more efficient. The high precision of SecFloat leads to the first accurate implementation of secure inference. All prior works on secure inference of deep neural networks rely on ad hoc float-to-fixed converters. We evaluate a model where the fixed-point approximations used in privacy-preserving machine learning completely fail and floating-point is necessary. Thus, emphasizing the need for libraries like SecFloat.

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs) use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal of square root. Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication. We provide new specialized 2PC protocols for math functions that crucially rely on lookup-tables and mixed-bitwidths to address this performance overhead; our protocols for math functions communicate up to 423× less data than prior work. Furthermore, our math implementations are numerically precise, which ensures that the secure implementations preserve model accuracy of cleartext. We build on top of our novel protocols to build SiRnn, a library for end-to-end secure 2-party DNN inference, that provides the first secure implementations of an RNN operating on time series sensor data, an RNN operating on speech data, and a state-of-the-art ML architecture that combines CNNs and RNNs for identifying all heads present in images. Our evaluation shows that SiRnn achieves up to three orders of magnitude of performance improvement when compared to inference of these models using an existing state-of-the-art 2PC framework.

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct -- i.e., their outputs are bitwise equivalent to the cleartext execution -- and efficient -- they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.

Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F ‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

A wildcard character in a pattern adds an additional feature in the field of pattern matching. In this paper, we consider two problems of secure pattern matching (SPM) with wildcards: (i) SPM with repetitive wildcards (SPM-RW) and (ii) SPM with compound wildcards (SPM-CW). Here we consider that a type of wildcard characters “*” is used to represent gaps in the pattern for the first problem of SPM with wildcards. Usually, a wildcard character “*” is used to replace with zero or more letters in the text for the pattern matching problem. Yasuda et al. (ACISP 2014) proposed a protocol with an existing data packing method for secure wildcards pattern matching using symmetric somewhat homomorphic encryption (SwHE) in the semi-honest model in which a wildcard character in the pattern is replaced with just one letter in the text. Furthermore, we enhance their work to replace a wildcard with any sequence of letters in the text then propose SPM-RW protocols by using the symmetric and public-key SwHE schemes in the semi-honest model. Also, we propose a packing method that improves the number of homomorphic multiplications by a factor of k compared to a naive usage of Yasuda et al.’s method to solve the SPM-RW problem in which k is the number of sub-patterns. Next, we consider the SPM-CW problem for processing private database queries, which allows a few types of wildcards (“$”, “*”, and “!”) to appear in the pattern. To solve this problem, we propose an SPM-CW protocol using a double-query technique with public-key SwHE encryption in the semi-honest model. Our experiments exhibit the practicality of the new protocols for SPM-RW and SPM-CW, which outperforms state-of-the-art.