CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption

Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is impractically slow, preventing it from being used in real applications. A major computational bottleneck in HE is the key-switching operation, accounting for approximately 70% of the overall HE execution time and involving a large amount of data for inputs, intermediates, and keys. Prior research has focused on hardware accelerators to improve HE performance, typically featuring large on-chip SRAMs and high off-chip bandwidth to deal with large scale data. In this paper, we present a novel approach to improve key-switching performance by rigorously analyzing its dataflow. Our primary goal is to optimize data reuse with limited on-chip memory to minimize off-chip data movement. We introduce three distinct dataflows: Max-Parallel (MP), Digit-Centric (DC), and Output-Centric (OC), each with unique scheduling approaches for key-switching computations. Through our analysis, we show how our proposed Output-Centric technique can effectively reuse data by significantly lowering the intermediate key-switching working set and alleviating the need for massive off-chip bandwidth. We thoroughly evaluate the three dataflows using the RPU, a recently published vector processor tailored for ring processing algorithms, which includes HE. This evaluation considers sweeps of bandwidth and computational throughput, and whether keys are buffered on-chip or streamed. With OC, we demonstrate up to 4.16x speedup over the MP dataflow and show how OC can save 12.25x on-chip SRAM by streaming keys for minimal performance penalty

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

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

The outbreak of post-traumatic stress disturbances during the COVID-19 pandemic: A systematic review

Although almost three years have passed since the outbreak of the coronavirus (COVID-19), this unprecedented situation is still not under control. Since COVID-19 has the potential to harm the human body, this systematic review aimed to evaluate the outbreak of post-traumatic stress disturbance (PTSD) during the COVID-19 epidemic. We used the search strategy of “novel coronavirus” OR “2019 novel coronavirus” OR “novel coronavirus pneumonia” OR “new coronavirus” OR “coronavirus disease 2019” OR “SARS2” OR “2019-n CoV” OR “SARS-CoV-2” OR “COVID-19” AND “PTSD” OR “PTS” OR “post-traumatic stress” OR “mental disorders”. The exclusion criteria included: a) articles that were not in English or Persian language; b) articles whose full text was not available, c) articles that did not report the prevalence of PTSD, d) articles that were not specific to COVID-19 or included other diseases, e) duplicate publications; f) reviews, abstracts, case reports, case series, and g) studies with target groups other than healthcare workers (HCWs), patients with COVID-19 and general population. After reviewing the articles and checking the exclusion criteria, the full text of 27 articles was reviewed. The studies showed the prevalence of PTSD in the HCW, general population and COVID-19 patients varied from the lowest to the highest as 3.8% to 56.6%, 4.6% to 67.09% and 5.61% to 96.2%, respectively. Given the prevalence of PTSD associated to COVID-19 in the investigated groups, it is recommended to design and implement educational and interventional programs to manage stress and deal with stressful situations such as epidemics

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

Majority Voting of Doctors Improves Appropriateness of AI Reliance in Pathology

As Artificial Intelligence (AI) making advancements in medical decision-making, there is a growing need to ensure doctors develop appropriate reliance on AI to avoid adverse outcomes. However, existing methods in enabling appropriate AI reliance might encounter challenges while being applied in the medical domain. With this regard, this work employs and provides the validation of an alternative approach -- majority voting -- to facilitate appropriate reliance on AI in medical decision-making. This is achieved by a multi-institutional user study involving 32 medical professionals with various backgrounds, focusing on the pathology task of visually detecting a pattern, mitoses, in tumor images. Here, the majority voting process was conducted by synthesizing decisions under AI assistance from a group of pathology doctors (pathologists). Two metrics were used to evaluate the appropriateness of AI reliance: Relative AI Reliance (RAIR) and Relative Self-Reliance (RSR). Results showed that even with groups of three pathologists, majority-voted decisions significantly increased both RAIR and RSR -- by approximately 9% and 31%, respectively -- compared to decisions made by one pathologist collaborating with AI. This increased appropriateness resulted in better precision and recall in the detection of mitoses. While our study is centered on pathology, we believe these insights can be extended to general high-stakes decision-making processes involving similar visual tasks.Comment: 46 pages, 11 figures. Accepted International Journal of Human-Computer Studie

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