Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference

Secure multi-party computation (MPC) allows users to offload machine learning inference on untrusted servers without having to share their privacy-sensitive data. Despite their strong security properties, MPC-based private inference has not been widely adopted in the real world due to their high communication overhead. When evaluating ReLU layers, MPC protocols incur a significant amount of communication between the parties, making the end-to-end execution time multiple orders slower than its non-private counterpart. This paper presents HummingBird, an MPC framework that reduces the ReLU communication overhead significantly by using only a subset of the bits to evaluate ReLU on a smaller ring. Based on theoretical analyses, HummingBird identifies bits in the secret share that are not crucial for accuracy and excludes them during ReLU evaluation to reduce communication. With its efficient search engine, HummingBird discards 87--91% of the bits during ReLU and still maintains high accuracy. On a real MPC setup involving multiple servers, HummingBird achieves on average 2.03--2.67x end-to-end speedup without introducing any errors, and up to 8.64x average speedup when some amount of accuracy degradation can be tolerated, due to its up to 8.76x communication reduction

Complete response of skull base inverted papilloma to chemotherapy: Case report.

BackgroundInverted papilloma (IP) is the most common benign sinonasal neoplasm. Endoscopic techniques, improved understanding of pathophysiology, and novel surgical approaches have allowed rhinologists to treat IPs more effectively, with surgery being the mainstay of therapy. Frontal sinus IP poses a challenge for surgical therapy due to complex anatomy and potentially difficult surgical access.ObjectivesWe reported a unique case of a massive frontal sinus IP that presented with intracranial and orbital extension, with near resolution after chemotherapy.MethodsA retrospective case review of a patient with a frontal sinus IP treated at a tertiary academic medical center.ResultsA 75-year-old male patient presented with nasal obstruction, purulent nasal discharge, and a growing left supraorbital mass. Endoscopy demonstrated a mass that filled both frontal and ethmoid sinuses, with orbital invasion. There also was substantial erosion of the posterior table, which measured 1.73 × 1.40 cm. A biopsy specimen demonstrated IP with carcinoma in situ. The patient was deemed unresectable on initial evaluation and, subsequently, underwent chemotherapy (carboplatin and paclitaxel). The tumor had a dramatic response to chemotherapy, and the patient elected for definitive surgery to remove any residual disease. During surgery, only a small focus of IP was found along the superior wall of the frontal sinus. No tumor was found elsewhere, including at the site of skull base erosion. The final pathology was IP without carcinoma in situ or dysplasia.ConclusionThis was the first reported case of chemotherapeutic "debulking" of IP, which facilitated surgical resection, despite substantial intracranial and orbital involvement. Although nearly all IPs can be treated surgically, rare cases, such as unresectable tumors, may benefit from systemic chemotherapy

Incidental finding of lymphoma after septoplasty.

IntroductionSeptoplasty, or surgical correction of the deviated septum, is an elective, routinely performed rhinologic procedure to address nasal airway obstruction. In many cases, resected septal cartilage and bone fragments are sent for pathologic review, although there is no consensus on this practice. We reported two cases of incidentally diagnosed lymphoma after elective septoplasty and discussed clinical presentation, diagnosis, and management.MethodsRetrospective chart review of two patients who underwent septoplasty at a tertiary academic medical center and found to have incidental lymphoma based on histopathology.ResultsTwo patients who underwent septoplasty had an incidental diagnosis of lymphoma on pathologic analysis. One patient was noted to have an S-shaped septal deviation that produced bilateral nasal obstruction. She underwent a difficult septoplasty, in which the mucoperichondrial flap was firmly adherent to the underlying septum and bone. Final pathology demonstrated diffuse large B-cell lymphoma. She was treated with chemoradiation and remained free of disease at 59 months. The other patient had a history of nasal trauma, which produced left septal deviation. He underwent an uncomplicated septoplasty, with pathology that demonstrated low-grade B-cell lymphoma. Because there was no evidence of active disease, the decision was made to not treat and to observe the patient clinically.ConclusionsThis is the first reported series of septal lymphoma incidentally diagnosed on routine septoplasty. Although histopathologic review of specimens from routine nasal and sinus surgery is not routinely performed, this report highlighted the importance of this process, on a case-by-case basis, in detecting unexpected malignancies that otherwise were clinically silent

GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning

This paper proposes GuardNN, a secure deep neural network (DNN) accelerator, which provides strong hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a specific application to provide strong confidentiality and integrity protection with negligible overhead. The design of the GuardNN instruction set reduces the TCB to just the accelerator and enables confidentiality protection without the overhead of integrity protection. GuardNN also introduces a new application-specific memory protection scheme to minimize the overhead of memory encryption and integrity verification. The scheme shows that most of the off-chip meta-data in today's state-of-the-art memory protection can be removed by exploiting the known memory access patterns of a DNN accelerator. GuardNN is implemented as an FPGA prototype, which demonstrates effective protection with less than 2% performance overhead for inference over a variety of modern DNN models

MgX: Near-Zero Overhead Memory Protection with an Application to Secure DNN Acceleration

In this paper, we propose MgX, a near-zero overhead memory protection scheme for hardware accelerators. MgX minimizes the performance overhead of off-chip memory encryption and integrity verification by exploiting the application-specific aspect of accelerators. Accelerators tend to explicitly manage data movement between on-chip and off-chip memory, typically at an object granularity that is much larger than cache lines. Exploiting these accelerator-specific characteristics, MgX generates version numbers used in memory encryption and integrity verification only using on-chip state without storing them in memory, and also customizes the granularity of the memory protection to match the granularity used by the accelerator. To demonstrate the applicability of MgX, we present an in-depth study of MgX for deep neural network (DNN) and also describe implementations for H.264 video decoding and genome alignment. Experimental results show that applying MgX has less than 1% performance overhead for both DNN inference and training on state-of-the-art DNN architectures

Analysis Without Data: Teaching Students to Tackle the VAST Challenge

The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle at identifying a good "starting point" when tackling the VAST Challenge. Consequently, students who could not identify a good starting point failed at finding the correct solution to the challenge. In this paper, we propose a preliminary guideline for helping students approach the VAST Challenge and identify initial analysis directions. We recruited two students to analyze the VAST 2017 Challenge using a hypothesis-driven approach, where they were required to pre-register their hypotheses prior to inspecting and analyzing the full dataset. From their experience, we developed a prescriptive guideline for other students to tackle VAST Challenges. In a preliminary study, we found that the students were able to use the guideline to generate well-formed hypotheses that could lead them towards solving the challenge. Additionally, the students reported that with the guideline, they felt like they had concrete steps that they could follow, thereby alleviating the burden of identifying a good starting point in their analysis process.Comment: IEEE Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides

CLEVER: Gamification and Enterprise Knowledge Learning

© Lennart Nacke, 2016. This is the author’s version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in CHI PLAY Companion '16 Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, https://doi.org/10.1145/2968120.2987745This paper describes the design and a preliminary implementation study of a gamified knowledge management system (KMS) that supports the learning component within knowledge management (KM). KM includes acquiring social capital through the process of acquisition, sharing, and dissemination of knowledge within a company. Employees often lack the motivation to share their implicit knowledge with one another and are reluctant to engage in a collaborative forum for such knowledge exchange. We developed a gamified learning component of an enterprise KMS to help foster this process of collaborative and participatory learning. More importantly, this game combines trivia and strategy elements as game elements to motivate the players for knowledge exchange. We report preliminary results from an exploratory study with nine participants which indicates that the above combination of game elements does contribute to participatory knowledge learning within an enterprise KMS.NSERC SSHRCPeer-reviewe