Provably secure and efficient audio compression based on compressive sensing

The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security

The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Tumour mutational burden (TMB) predicts immunotherapy outcome in non-small cell lung cancer (NSCLC), consistent with immune recognition of tumour neoantigens. However, persistent antigen exposure is detrimental for T cell function. How TMB affects CD4 and CD8 T cell differentiation in untreated tumours and whether this affects patient outcomes is unknown. Here, we paired high-dimensional flow cytometry, exome, single-cell and bulk RNA sequencing from patients with resected, untreated NSCLC to examine these relationships. TMB was associated with compartment-wide T cell differentiation skewing, characterized by loss of TCF7-expressing progenitor-like CD4 T cells, and an increased abundance of dysfunctional CD8 and CD4 T cell subsets with strong phenotypic and transcriptional similarity to neoantigen-reactive CD8 T cells. A gene signature of redistribution from progenitor-like to dysfunctional states was associated with poor survival in lung and other cancer cohorts. Single-cell characterization of these populations informs potential strategies for therapeutic manipulation in NSCLC

Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers

Background: Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most commonunderlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis.Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiplevariables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed atidentifying a biomarker signature to predict particular sites of DM in TNBC.Methods: A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, todevelop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasisto each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Coxunivariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariableanalyses.Results: Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher riskof developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predictingsite-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status.Conclusions: Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specificsites of metastasis, and potentially unravel biomarkers previously unknown in site tropism

Prognostic significance of androgen receptor expression in invasive breast cancer: transcriptomic and protein expression analysis

Differential prognostic roles of Androgen Receptor (AR) have been proposed in breast cancer (BC) depending on tumour oestrogen receptor (ER) status. This study aimed to evaluate the prognostic and/or predictive significance of AR expression in invasive BC. In this study AR expression was studied on a large (n = 1141) consecutive series of early-stage (I-III) BC using tissue microarray and immunohistochemistry (IHC). AR mRNA expression was assessed in a subset of cases. The prognostic impact of AR mRNA expression was externally validated using the online BC gene expression data sets (n = 25 data sets, 4078 patients). Nuclear AR IHC expression was significantly associated with features of good prognosis including older age, smaller tumour size, lower grade and lobular histology particularly in the ER-positive tumours. AR was associated with ER-related markers GATA3, FOXa1, RERG and BEX1. Negative association was observed with HER2, p53, Ki67, TK1, CD71 and AGTR1. AR Overexpression was associated with longer survival (p < 0.001), independent of tumour size, grade, stage [p = 0.033, hazard ratio (HR) = 0.80 95 % CI = 0.64-0.98]. Similar associations were maintained in ER+ tumours in univariate and multivariate analysis (p < 0.01) both in patients with and without adjuvant endocrine or chemotherapy. AR mRNA expression showed significant association with tumour grade, molecular subtypes, and longer 10 and 15 years survival in luminal BC. In the external validation cohorts, AR gene expression data were associated with improved patients' outcome (p < 0.001, HR = 0.84, 95 % CI 0.79-0.90). AR is not only an independent prognostic factor in ER-positive luminal BC but is also expressed in ER-negative tumours. AR could act as a molecular target in patients with ER-positive disease predicting response to adjuvant therapy

Fast Multi-User Searchable Encryption with Forward and Backward Private Access Control

Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model

Lightweight, Secure, Similar-Document Retrieval over Encrypted Data

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches

Lightweight, Secure, Similar-Document Retrieval over Encrypted Data

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches

A Lightweight Hybrid Scheme for Hiding Text Messages in Colour Images Using LSB, Lah Transform and Chaotic Techniques

Data security can involve embedding hidden images, text, audio, or video files within other media to prevent hackers from stealing encrypted data. Existing mechanisms suffer from a high risk of security breaches or large computational costs, however. The method proposed in this work incorporates low-complexity encryption and steganography mechanisms to enhance security during transmission while lowering computational complexity. In message encryption, it is recommended that text file data slicing in binary representation, to achieve different lengths of string, be conducted before text file data masking based on the lightweight Lucas series and mod function to ensure the retrieval of text messages is impossible. The steganography algorithm starts by generating a random key stream using a hybrid of two low-complexity chaotic maps, the Tent map and the Ikeda map. By finding a position vector parallel to the input image vector, these keys are used based on the previously generated position vector to randomly select input image data and create four vectors that can be later used as input for the Lah transform. In this paper, we present an approach for hiding encrypted text files using LSB colour image steganography by applying a low-complexity XOR operation to the most significant bits in 24-bit colour cover images. It is necessary to perform inverse Lah transformation to recover the image pixels and ensure that invisible data cannot be retrieved in a particular sequence. Evaluation of the quality of the resulting stego-images and comparison with other ways of performing encryption and message concealment shows that the stego-image has a higher PSNR, a lower MSE, and an SSIM value close to one, illustrating the suitability of the proposed method. It is also considered lightweight in terms of having lower computational overhead