Private Web Search with Constant Round Efficiency

Web search is increasingly becoming an essential activity as it is frequently the most effective and convenient way of finding information. However, it can be a threat for the privacy of users because their queries may reveal their sensitive information. Private web search (PWS) solutions allow users to find information in the Internet while preserving their privacy. In particular, cryptography-based PWS (CB-PWS) systems provide strong privacy guarantees. This paper introduces a constant-round CB-PWS protocol which remains computationally efficient, compared to known CB-PWS systems. Our construction is comparable to similar solutions regarding users\u27 privacy

Ghostshell: Secure Biometric Authentication using Integrity-based Homomorphic Evaluations

Biometric authentication methods are gaining popularity due to their convenience. For an authentication without relying on trusted hardwares, biometrics or their hashed values should be stored in the server. Storing biometrics in the clear or in an encrypted form, however, raises a grave concern about biometric theft through hacking or man-in-the middle attack. Unlike ID and password, once lost biometrics cannot practically be replaced. Encryption can be a tool for protecting them from theft, but encrypted biometrics should be recovered for comparison. In this work, we propose a secure biometric authentication scheme, named Ghostshell, in which an encrypted template is stored in the server and then compared with an encrypted attempt \emph{without} decryption. The decryption key is stored only in a user\u27s device and so biometrics can be kept secret even against a compromised server. Our solution relies on a somewhat homomorphic encryption (SHE) and a message authentication code (MAC). Because known techniques for SHE is computationally expensive, we develop a more practical scheme by devising a significantly efficient matching function exploiting SIMD operations and a one-time MAC chosen for efficient homomorphic evaluations (of multiplication depth 2). When applied to Hamming distance matching on 2400-bit irises, our implementation shows that the computation time is approximately 0.47 and 0.1 seconds for the server and the user, respectively

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal.Ragon Institute of MGH, MIT and HarvardSamsung Scholarship FoundationNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Optimizing vaccine dosing kinetics for stronger antibody response

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2019Cataloged from PDF version of thesis. "The pagination in this thesis reflects how it was delivered to the Institute Archives and Special Collections. The Table of Contents does not accurately represent the page numbering"--Disclaimer Notice page.Includes bibliographical references (pages 95-102).One of the barriers to rational vaccine design against evolving pathogens is our lack of mechanistic understanding of how innate and adaptive immune response systematically emerge and evolve. Immune response is comprised of dynamic events that require many components to cooperate collectively in a manner that spans a range of scales. These characteristics make it hard to predict mechanisms for immune response based solely on experimental observations. This thesis investigates various aspects of affinity maturation that are relevant to vaccination and therapeutic strategies but are not yet fully understood mechanistically, ranging from the evolution of the heterogeneity of the antibody population with respect to affinity to optimal design parameters for temporal dosing of vaccines. Our approach is to apply computational techniques to mathematically model the immune system, and being synergistic with complementary experiments. 1.As affinity maturation ensues, average affinity of antibodies increase with time while resulting affinity distribution becomes increasingly heterogeneous. To shed light on how the extent of this heterogeneity evolves with time during affinity maturation, we have taken advantage of previously published data of antibodies isolated from individual serum samples. Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we find that after a single injection of small antigen doses, the ratio decreases progressively over time. This is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal. 2.What are the aspects of affinity maturation being altered by various temporal patterns of antigen dosing? Certain extended-duration dosing profiles increase the strength of the humoral response, with exponentially-increasing(EI) dosage providing the greatest enhancement. While this is an exciting result, it is necessary to establish a mechanistic understanding of how immune response be enhanced to further engineer and optimize the temporal patterns. From our computational model, the effect is driven by enhanced capture of antigen in lymph nodes by evolving higher-affinity antibodies early in the GC response. We validate the prediction from independent experimental data, where EI dosage result in promoted capture and retention of the antigen in lymph nodes. To our knowledge, this work is the first to demonstrate a key mechanism for vaccine kinetics in the response of B cells to immunization, and may prove to be an effective method for increasing the efficacy of subunit vaccines. 3.Are there optimal dosing profiles that maximize total protection? That is, lead to the evolution of the most antibodies of high affinity? In extension of mechanistic studies in 2, we propose a stochastic simulation method that can be used as a tool for optimizing dosage protocols for vaccine delivery. Using this tool, we analyze experimental conditions for EI dosage induce suboptimal immune response and investigate two approaches for the optimization. Specifically, reducing the total dosage optimizes affinity of resulting antibodies, while total protection is optimal neither at constant or EI dosage but that corresponding to a "linear-like" dosing profile. Our approach can be extended to broader applications in vaccine design.by Myungsun (Sunny) Kang.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Chemical Engineerin

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal

Affinity Inequality among Serum Antibodies That Originate in Lymphoid Germinal Centers.

Upon natural infection with pathogens or vaccination, antibodies are produced by a process called affinity maturation. As affinity maturation ensues, average affinity values between an antibody and ligand increase with time. Purified antibodies isolated from serum are invariably heterogeneous with respect to their affinity for the ligands they bind, whether macromolecular antigens or haptens (low molecular weight approximations of epitopes on antigens). However, less is known about how the extent of this heterogeneity evolves with time during affinity maturation. To shed light on this issue, we have taken advantage of previously published data from Eisen and Siskind (1964). Using the ratio of the strongest to the weakest binding subsets as a metric of heterogeneity (or affinity inequality), we analyzed antibodies isolated from individual serum samples. The ratios were initially as high as 50-fold, and decreased over a few weeks after a single injection of small antigen doses to around unity. This decrease in the effective heterogeneity of antibody affinities with time is consistent with Darwinian evolution in the strong selection limit. By contrast, neither the average affinity nor the heterogeneity evolves much with time for high doses of antigen, as competition between clones of the same affinity is minimal