Differentially private stochastic gradient descent algorithm for multiparty classification

We consider the problem of developing privacy-preserving machine learning algorithms in a dis-tributed multiparty setting. Here different parties own different parts of a data set, and the goal is to learn a classifier from the entire data set with-out any party revealing any information about the individual data points it owns. Pathak et al [7]recently proposed a solution to this problem in which each party learns a local classifier from its own data, and a third party then aggregates these classifiers in a privacy-preserving manner using a cryptographic scheme. The generaliza-tion performance of their algorithm is sensitive to the number of parties and the relative frac-tions of data owned by the different parties. In this paper, we describe a new differentially pri-vate algorithm for the multiparty setting that uses a stochastic gradient descent based procedure to directly optimize the overall multiparty ob-jective rather than combining classifiers learned from optimizing local objectives. The algorithm achieves a slightly weaker form of differential privacy than that of [7], but provides improved generalization guarantees that do not depend on the number of parties or the relative sizes of the individual data sets. Experimental results corrob-orate our theoretical findings.

Is The Burden of Medical Education Affecting Ethics of Medical Treatment?

A substantial number of medical students in India have to bear an enormous financial burden for earning a bachelor's degree in medicine referred to as MBBS (bachelor of medicine and bachelor of surgery). This degree program lasts for four and one-half years followed by one year of internship. A postgraduate degree, such as MD, has to be pursued separately on completion of a MBBS. Every medical college in India is part of a hospital where the medical students get clinical exposure during the course of their study. All or at least a number of medical colleges in a given state are affiliated to a university that mainly plays a role of an overseeing authority. The medical colleges usually have no official interaction with other disciplines of education such as science and engineering, perhaps because of their independent location and absence of emphasis on medical research. However, many of the medical colleges are adept in imparting high-quality and sound training in medical practices including diagnostics and treatment. The medical colleges in India are generally of two types, i.e., government owned and private. Since only a limited number of seats are available across India in the former category of colleges, only a small fraction of aspiring candidates can find admission in these colleges after performing competitively in the relevant entrance tests. A major advantage of studying in these colleges is the nominal tuition fees that have to be paid. On the other hand, a large majority of would-be medical graduates have to seek admission in the privately run medical institutes in which the tuition and other related fees can be mind boggling when compared to their public counterparts. Except for candidates of exceptionally affluent background, the only alternative for fulfilling the dream of becoming a doctor is by financing one's study through hefty bank loans that may take years to pay back. It is often heard from patients that they are asked by doctors to undergo a plethora of diagnostic tests for apparently minor illnesses, which may financially benefit those prescribing the tests. The present paper attempts to throw light on the extent of disparity in cost of a medical education between state-funded and privately managed medical colleges in India; the average salary of a new medical graduate, which is often ridiculously low when compared to what is offered in entry-level engineering and business jobs; and the possible repercussions of this apparently unjust economic situation regarding the exploitation of patients.