Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation

We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340ms in the case where the dictionary size of Bob's model includes all words (n = 5200) and Alice's SMS has at most m = 160 unigrams. In the case with n = 369 and m = 8 (the average of a spam SMS in the database), our solution takes only 21ms

Los materiales usados en escuelas de Educación Infantil proyectados en sus webs y blogs

En España, la Educación Infantil (EI) no es una etapa de escolarización obligatoria, pero, en la actualidad casi el 100% del alumnado de 3-6 años está escolarizado. En los últimos años en la comunidad científica se ha despertado un interés por investigar sobre diferentes aspectos curriculares relacionados con la praxis educativa de esta etapa. En este marco, los materiales juegan un rol fundamental como mediadores para el acceso al conocimiento y también para el desarrollo de proyectos educativos. En paralelo, las políticas de incentivación del uso de las TIC en la sociedad y en la escuela generan nuevas formas traspasar muros: sus webs y blogs sirven de escaparate para que los centros dejen constancia de su trabajo. Este artículo presenta los resultados de una investigación que tiene dos objetivos: Identificar el tipo de materiales proyectados en los blogs y website de los centros educativos, y comprobar si éstos están contextualizados en la comunidad educativa. En esta investigación se seleccionan cuatro colegios que responde a cuatro modelos representativos en cuanto al uso de materiales y de webs/blogs. Es una selección intencionada a partir de los siguientes criterios: tipo de escuela, contexto socieconómico, uso de sus webs/blogs y tipos de materiales. Es una muestra de 27 aulas agrupadas en cuatro centros. El instrumento de investigación se elaboró a partir de los indicadores propuestos por Booth and Ainscow (2011). Los resultados indican que existe una cultura de compartir materiales; uso mayoritario de materiales de fuera de la comunidad excepto en los centros rurales. El uso de los blogs y website difiere: unos centros reflejan procesos y otros presentan los productos

Training Differentially Private Models with Secure Multiparty Computation

We address the problem of learning a machine learning model from training data that originates at multiple data owners while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis

Privacy-preserving training of tree ensembles over continuous data

Abstract Most existing Secure Multi-Party Computation (MPC) protocols for privacy-preserving training of decision trees over distributed data assume that the features are categorical. In real-life applications, features are often numerical. The standard “in the clear” algorithm to grow decision trees on data with continuous values requires sorting of training examples for each feature in the quest for an optimal cut-point in the range of feature values in each node. Sorting is an expensive operation in MPC, hence finding secure protocols that avoid such an expensive step is a relevant problem in privacy-preserving machine learning. In this paper we propose three more efficient alternatives for secure training of decision tree based models on data with continuous features, namely: (1) secure discretization of the data, followed by secure training of a decision tree over the discretized data; (2) secure discretization of the data, followed by secure training of a random forest over the discretized data; and (3) secure training of extremely randomized trees (“extra-trees”) on the original data. Approaches (2) and (3) both involve randomizing feature choices. In addition, in approach (3) cut-points are chosen randomly as well, thereby alleviating the need to sort or to discretize the data up front. We implemented all proposed solutions in the semi-honest setting with additive secret sharing based MPC. In addition to mathematically proving that all proposed approaches are correct and secure, we experimentally evaluated and compared them in terms of classification accuracy and runtime. We privately train tree ensembles over data sets with thousands of instances or features in a few minutes, with accuracies that are at par with those obtained in the clear. This makes our solution more efficient than the existing approaches, which are based on oblivious sorting.</jats:p

A Critical Review of Randall Ryder’s Report of Direct Instruction Reading in Two Wisconsin School Districts

A recent report by Dr. Randall Ryder evaluated the use of Direct Instruction (DI) reading programs in 2 school districts in Wisconsin. In the report, Ryder claimed that students in 1st, 2nd, and 3rd grade who received Direct Instruction scored significantly lower on several standardized tests of reading than students who received more traditional reading instruction. This article examines the validity of the Ryder report. Examination of the report revealed that (a) the quality of implementation of Direct Instruction is highly suspect; (b) the group labeled Direct Instruction ; apparently included numerous students who received an undefined mix of DI and non-DI reading instruction; (c) the selection and assignment of classrooms and students to groups resulted in DI groups that performed substantially below the non-DI groups before the study began; (d) there are numerous ambiguities and contradictions regarding the number of students in various groups in each year of the study; (e) statistical reporting failed to include basic information such as degrees of freedom, means, and standard deviation for some or all analyses; (f) ANCOVA was assumed to control for system systematically biased assignment without consideration of the assumptions, limitations, and interpretive difficulties involved; and (g) Ryder fails to report results from subtests on which, in previous reports, the DI group outperformed the non-DI group by a statistically significant margin. As a result of these and other problems, no firm conclusions can be drawn from Ryder\u27s report. We conclude that Ryder\u27s report should be subjected to an independent peer review process, and the results of that process should be publicized as widely as the report has been

A late-Holocene climate record in stalagmites from Modrič Cave (Croatia)

Few terrestrial Holocene climate records exist from Southeastern Europe despite its important geographic position as a transitional climatic zone between the Mediterranean and mainland continental Europe. In this study we present new petrographic and stable isotope data for two Holocene speleothems from Modrič Cave, Croatia (44o15’N, 15o32’E), a coastal Adriatic site (120 metres inland). Modern meteorological and cave conditions have been monitored for two years to understand the links between the climate variability and the stable isotope time-series records in speleothems. Typical of a Mediterranean-type climate, a negative water balance exists between April and September, so that recharge of the aquifer is restricted to the winter months. The weighted mean δ18O of the rainfall is -5.96‰ (2σ =2.83), and the weighted mean D/H rainfall value is -36.83‰ (2σ = 19.95), slightly above the Global Meteoric Water Line (GMWL), but well below the Mediterranean Meteoric Water Line (MMWL). Modern calcite from the tops of each stalagmite exhibits δ18O values that are close to isotopic equilibrium with their respective drip water values. Unfortunately, the relatively young ages and low uranium contents (c. 50 ppb) of both stalagmites hamper the use of U-series dating. Radiocarbon dates have been used instead to constrain their chronology using a dead carbon correction. Aside from some Isotope Stage 3 material (c. 55 ka), both stalagmites were deposited during the late Holocene. Climatic conditions during the late Holocene are inferred to have been sufficiently wet to maintain stalagmite growth and any hiatuses appear to be relatively short lived. Inferred changes in the stalagmite diameters during deposition are linked to δ13C and δ18O variations, indicating alternating periods of drier and wetter conditions. Drier conditions are inferred for the late Roman Ages warm period and the mid-Medieval Warm Period (MWP). Wetter conditions are associated with the Little Ice Age period.Science Foundation Irelan