Towards Fast and Scalable Private Inference

Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads. This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI.Comment: Appear in the 20th ACM International Conference on Computing Frontier

Evaluation of the Effect of Mesenchymal Stem Cells on Breast Cancer Migration and Metastasis: A Systematic Review and Meta-analysis

Background and aim: Insufficient evidence in the field of the effect of MSCs on the migration of breast cancer cells caused the present study to be conducted with the consensus of the findings and to perform a meta-analysis to evaluate the effect of mesenchymal stem cells on breast cancer migration and metastasis.Material and methods: In the present systematic review and meta-analysis, information about mesenchymal stem cells in breast cancer patients in all articles published until the end of July 2023 through searching in databases PubMed, Scopus, Science Direct, ISI, Web of Knowledge, Elsevier, Wiley, and Embase and Google Scholar search engine were extracted using keywords and their combinations by two trained researchers independently. Data analysis was done using the fixed effects model in the meta-analysis by STATA (version 17); a p-value less than 0.05 was considered significant.Results: Thirteen in-vitro and in-vivo studies were included in the meta-analysis process. The risk ratio of incidence of metastasis after MSCs administration was 7.37 (RR, 95% CI: 7.23, 7.53; I2 =99.86% (p=0.00), very high heterogeneity); human-MSCs from different sources appear to increase the migratory activity of MDA-MB-231 cells and MCF-7 cells compared to control group(p<0.01). Conclusions: Meta-analysis showed that MSCs are significantly effective in increasing the migration of breast cancer cells and metastasis. Therefore, MSCs can be a promising option for treating breast cancer metastases

Prevalence of Spinal Deformities among School Age Children in Iran: A Systematic Review and Meta-Analysis

Background: Postural disorders and spinal deformities are one of the common conditions in children and adolescents. The aim of this systematic review and meta-analysis was to study the prevalence of spinal deformities among school age children in Iran.Methods: The search strategy was developed using keywords relating to kyphosis, lordosis, scoliosis, child and Iran in the databases of Medline, Scopus, CINAHL, Psycinfo as well as Persian local databases up to January 2020. Articles were appraised by two reviewers using the checklist of Joanna Briggs Institute (JBI) and data was extracted in the designed tables and analyzed using R software with a random effects model. The heterogeneity and dispersion of data was presented in Forest plots.Results: Eighteen studies were included in the meta-analysis. The total population included 84195 students consisting of 39202 boys and 45947 girls. The mean age of the participants was 12.71±1.18 years. The total prevalence of kyphosis was 13.06% [95% CI 0.07; 0.22], the total prevalence of scoliosis was 2.61% [95% CI 0.014; 0.045] and the total prevalence of lordosis was 32.59% [95% CI 0.23; 0.43]. The prevalence of deformities was higher in girls. Kyphosis and scoliosis was more frequent in elementary school children but lordosis was more frequent in middle school students. Confirmation of diagnosis with radiology as well as clinical examination yielded a lower prevalence compared to diagnosis only made by clinical examination.Conclusion: The prevalence of spinal deformities in school age children in Iran is on the average level compared to the other countries and lordosis is more common in girls. Designing further studies to evaluate etiology and risk factors of this condition is recommende

Compliance With Guideline Statements for Urethral Catheterization in an Iranian Teaching Hospital

Background: It is believed that healthcare staff play an important role in minimizing complications related to urethral catheterization. The purpose of this study was to determine whether or not healthcare staff complied with the standards for urethral catheterization. Methods: This study was conducted in Imam Reza teaching hospital, Tabriz, Iran, from July to September 2013. A total of 109 catheterized patients were selected randomly from surgical and medical wards and intensive care units (ICUs). A questionnaire was completed by healthcare staff for each patient to assess quality of care provided for catheter insertion, while catheter in situ, draining and changing catheter bags. Items of the questionnaire were obtained from guidelines for the prevention of infection. Data analysis was performed with SPSS 16. Results: The mean age of the patients was 50.54 ± 22.13. Of the 109 patients, 56.88% were admitted to ICUs. The mean duration of catheter use was 15.86 days. Among the 25 patients who had a urinalysis test documented in their hospital records, 11 were positive for urinary tract infection (UTI). The lowest rate of hand-washing was reported before bag drainage (49.52%). The closed drainage catheter system was not available at all. Among the cases who had a daily genital area cleansing, in 27.63% cases, the patients or their family members performed the washing. In 66.35% of cases, multiple-use lubricant gel was applied; single-use gel was not available. The rate of documentation for bag change was 79%. Conclusion: The majority of the guideline statements was adhered to; however, some essential issues, such as hand hygiene were neglected. And some patients were catheterized routinely without proper indication. Limiting catheter use to mandatory situations and encouraging compliance with guidelines are recommended

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted during computation and secures the results, even in these untrusted environments. However, FHE requires a significant amount of computation to perform equivalent unencrypted operations. To be useful, FHE must significantly close the computation gap (within 10x) to make encrypted processing practical. To accomplish this ambitious goal the TREBUCHET project is leading research and development in FHE processing hardware to accelerate deep computations on encrypted data, as part of the DARPA MTO Data Privacy for Virtual Environments (DPRIVE) program. We accelerate the major secure standardized FHE schemes (BGV, BFV, CKKS, FHEW, etc.) at >=128-bit security while integrating with the open-source PALISADE and OpenFHE libraries currently used in the DoD and in industry. We utilize a novel tile-based chip design with highly parallel ALUs optimized for vectorized 128b modulo arithmetic. The TREBUCHET coprocessor design provides a highly modular, flexible, and extensible FHE accelerator for easy reconfiguration, deployment, integration and application on other hardware form factors, such as System-on-Chip or alternate chip areas.Comment: 6 pages, 5figures, 2 table

Enhancing Mitosis Quantification and Detection in Meningiomas With Computational Digital Pathology

Mitosis is a critical criterion for meningioma grading. However, pathologists\u27 assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists\u27 mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm\u27s ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management

RPU: The Ring Processing Unit

Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cryptography. While promising, these techniques have received limited use due to their extreme overheads of running on general-purpose machines. In this paper, we present a novel vector Instruction Set Architecture (ISA) and microarchitecture for accelerating the ring-based computations of RLWE. The ISA, named B512, is developed to meet the needs of ring processing workloads while balancing high-performance and general-purpose programming support. Having an ISA rather than fixed hardware facilitates continued software improvement post-fabrication and the ability to support the evolving workloads. We then propose the ring processing unit (RPU), a high-performance, modular implementation of B512. The RPU has native large word modular arithmetic support, capabilities for very wide parallel processing, and a large capacity high-bandwidth scratchpad to meet the needs of ring processing. We address the challenges of programming the RPU using a newly developed SPIRAL backend. A configurable simulator is built to characterize design tradeoffs and quantify performance. The best performing design was implemented in RTL and used to validate simulator performance. In addition to our characterization, we show that a RPU using 20.5mm2 of GF 12nm can provide a speedup of 1485x over a CPU running a 64k, 128-bit NTT, a core RLWE workloa

TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation

Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed. Traditional security techniques require data to be decrypted before performing any computation. When processed on untrusted systems the decrypted data is vulnerable to attacks to extract the sensitive information. To address these vulnerabilities Fully Homomorphic Encryption (FHE) keeps the data encrypted during computation and secures the results, even in these untrusted environments. However, FHE requires a significant amount of computation to perform equivalent unencrypted operations. To be useful, FHE must significantly close the computation gap (within 10x) to make encrypted processing practical. To accomplish this ambitious goal the TREBUCHET project is leading research and development in FHE processing hardware to accelerate deep computations on encrypted data, as part of the DARPA MTO Data Privacy for Virtual Environments (DPRIVE) program. We accelerate the major secure standardized FHE schemes (BGV, BFV, CKKS, FHEW, etc.) at >=128-bit security while integrating with the open-source PALISADE and OpenFHE libraries currently used in the DoD and in industry. We utilize a novel tile-based chip design with highly parallel ALUs optimized for vectorized 128b modulo arithmetic. The TREBUCHET coprocessor design provides a highly modular, flexible, and extensible FHE accelerator for easy reconfiguration, deployment, integration and application on other hardware form factors, such as System-on-Chip or alternate chip area

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects