Protecting privacy of users in brain-computer interface applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations

Adenoid cystic carcinoma of the buccal mucosa: A rare clinical presentation

Of malignant tumors with a propensity to invade the perineural space, adenoid cystic carcinoma of the salivary glands is perhaps a well-known entity. Adenoid cystic carcinoma (ACC) is a rare, slow growing malignant salivary gland tumor that is characterized by indolent, locally invasive  growth with high propensity for local recurrence and distant metastasis. Upto 50% of these tumors occur in the intraoral minor salivary glands usually in the hard palate. Buccal mucosal tumors are relatively rare. The purpose of this article is to discuss an unusual case of adenoid cystic carcinoma of the buccal mucosa and review the pertinent literature

A quest for measuring intra operative blood loss in Maxillofacial surgery

Aim: To find out a simple, standardized method to measure intra-operative blood loss during major oral surgical procedures which alerts the clinicians to manage untoward outcomes in time. Materials & Method: Patients who underwent surgical intervention for various dentofacial deformities, maxillofacial pathologies, maxillofacial trauma under general anesthesia via an intra oral approach from Jan 2014 – Aug 2015 were included in the study. Thirty such patients belonging to the above entities were randomly categorized into 2 groups of 15 each based on the method of measuring the intra op blood loss. In Group A the blood loss was measured by Sahlis method and in Group B, the blood loss was measured by cyanomethemoglobin method. All the procedures were performed via an intra oral approach under general anesthesia. Results: The amount of intra operative blood loss measured through Sahli’s method appeared to be insensitive and not standardized. However, the one measured through Cyanomethemoglobin method was more accurate, standardized and easy to perform. Conclusion: Cyanomethemoglobin method is an accurate, reliable, chair side, inexpensive, easy to perform, standardized technique to measure the intra operative blood loss in the recent times

Cancer epithelia-derived mitochondrial DNA is a targetable initiator of a paracrine signaling loop that confers taxane resistance.

Stromal-epithelial interactions dictate cancer progression and therapeutic response. Prostate cancer (PCa) cells were identified to secrete greater concentration of mitochondrial DNA (mtDNA) compared to noncancer epithelia. Based on the recognized coevolution of cancer-associated fibroblasts (CAF) with tumor progression, we tested the role of cancer-derived mtDNA in a mechanism of paracrine signaling. We found that prostatic CAF expressed DEC205, which was not expressed by normal tissue-associated fibroblasts. DEC205 is a transmembrane protein that bound mtDNA and contributed to pattern recognition by Toll-like receptor 9 (TLR9). Complement C3 was the dominant gene targeted by TLR9-induced NF-κB signaling in CAF. The subsequent maturation complement C3 maturation to anaphylatoxin C3a was dependent on PCa epithelial inhibition of catalase in CAF. In a syngeneic tissue recombination model of PCa and associated fibroblast, the antagonism of the C3a receptor and the fibroblastic knockout of TLR9 similarly resulted in immune suppression with a significant reduction in tumor progression, compared to saline-treated tumors associated with wild-type prostatic fibroblasts. Interestingly, docetaxel, a common therapy for advanced PCa, further promoted mtDNA secretion in cultured epithelia, mice, and PCa patients. The antiapoptotic signaling downstream of anaphylatoxin C3a signaling in tumor cells contributed to docetaxel resistance. The inhibition of C3a receptor sensitized PCa epithelia to docetaxel in a synergistic manner. Tumor models of human PCa epithelia with CAF expanded similarly in mice in the presence or absence of docetaxel. The combination therapy of docetaxel and C3 receptor antagonist disrupted the mtDNA/C3a paracrine loop and restored docetaxel sensitivity

Heterogeneous cancer-associated fibroblast population potentiates neuroendocrine differentiation and castrate resistance in a CD105-dependent manner.

Heterogeneous prostatic carcinoma-associated fibroblasts (CAF) contribute to tumor progression and resistance to androgen signaling deprivation therapy (ADT). CAF subjected to extended passaging, compared to low passage CAF, were found to lose tumor expansion potential and heterogeneity. Cell surface endoglin (CD105), known to be expressed on proliferative endothelia and mesenchymal stem cells, was diminished in high passage CAF. RNA-sequencing revealed SFRP1 to be distinctly expressed by tumor-inductive CAF, which was further demonstrated to occur in a CD105-dependent manner. Moreover, ADT resulted in further expansion of the CD105+ fibroblastic population and downstream SFRP1 in 3-dimensional cultures and patient-derived xenograft tissues. In patients, CD105+ fibroblasts were found to circumscribe epithelia with neuroendocrine differentiation. CAF-derived SFRP1, driven by CD105 signaling, was necessary and sufficient to induce prostate cancer neuroendocrine differentiation in a paracrine manner. A partially humanized CD105 neutralizing antibody, TRC105, inhibited fibroblastic SFRP1 expression and epithelial neuroendocrine differentiation. In a novel synthetic lethality paradigm, we found that simultaneously targeting the epithelia and its microenvironment with ADT and TRC105, respectively, reduced castrate-resistant tumor progression, in a model where either ADT or TRC105 alone had little effect

Text-Free Audio Captions of Short Videos from Latent Space Representation

In this thesis, we re-implement previous work exploring image to speech captioning. We expand upon the work to implement video to speech captioning. Specifically, we implement a text-free image to speech captioning pipeline that integrates four distinct machine learning models. We alter the models to process video data rather than image data and analyze the resulting speech captions. We conduct experiments on the Wav2Vec2 and HuBERT Automatic Speech Recognition models, and identify which works best with synthesized speech.M.Eng

Privacy-Preserving User Profiling With Facebook Likes

The content generated by users on social media is rich in personal information that can be mined to construct accurate user profiles, and subsequently used for tailored advertising or other personalized services. Facebook has recently come under scrutiny after a third party gained access to the data of millions of users and mined it to construct psychographical profiles, which were allegedly used to influence voters in elections. As part of a possible solution to avoid data breaches while still being able to perform meaningful machine learning (ML) on social media data, we propose a privacy-preserving algorithm for k-nearest neighbor (kNN) [1] , one of the oldest ML methods, used traditionally in collaborative filtering recommender systems

Protecting Privacy of Users in Brain-Computer Interface Applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e.~such that each individual\u27s EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations