Information-Theoretically Secure Protocols and Security Under Composition

We investigate the question of whether security of protocols in the information-theoretic setting (where the adversary is computationally unbounded) implies the security of these protocols under concurrent composition. This question is motivated by the folklore that all known protocols that are secure in the information-theoretic setting are indeed secure under concurrent composition. We provide answers to this question for a number of different settings (i.e., considering perfect versus statistical security, and concurrent composition with adaptive versus fixed inputs). Our results enhance the understanding of what is necessary for obtaining security under composition, as well as providing tools (i.e., composition theorems) that can be used for proving the security of protocols under composition while considering only the standard stand-alone definitions of security

Additive Randomized Encodings and Their Applications

Addition of $n$ inputs is often the easiest nontrivial function to compute securely. Motivated by several open questions, we ask what can be computed securely given only an oracle that computes the sum. Namely, what functions can be computed in a model where parties can only encode their input locally, then sum up the encodings over some Abelian group \G, and decode the result to get the function output. An *additive randomized encoding* (ARE) of a function $f(x_1,\ldots,x_n)$ maps every input $x_i$ independently into a randomized encoding $\hat x_i$, such that $\sum_{i=1}^n$ $\hat x_i$ reveals $f(x_1,\ldots,x_n)$ and nothing else about the inputs. In a *robust* ARE, the sum of any subset of the $\hat x_i$ only reveals the residual function obtained by restricting the corresponding inputs. We obtain positive and negative results on ARE. In particular: * Information-theoretic ARE. We fully characterize the 2-party functions $f:X_1\times X_2\to\{0,1\}$ admitting a perfectly secure ARE. For $n\ge 3$ parties, we show a useful ``capped sum\u27\u27 function that separates statistical security from perfect security. * Computational ARE. We present a general feasibility result, showing that *all functions* can be computed in this model, under a standard hardness assumption in bilinear groups. We also describe a heuristic lattice-based construction. * Robust ARE. We present a similar feasibility result for {\em robust} computational ARE based on ideal obfuscation along with standard cryptographic assumptions. We then describe several applications of ARE and the above results. * Under a standard cryptographic assumption, our computational ARE schemes imply the feasibility of general non-interactive secure computation in the shuffle model, where messages from different parties are shuffled. This implies a general utility-preserving compiler from differential privacy in the central model to computational differential privacy in the (non-robust) shuffle model. * The existence of information-theoretic {\em robust} ARE implies best-possible information-theoretic MPC protocols (Halevi et al., TCC 2018) and degree-2 multiparty randomized encodings (Applebaum et al., TCC 2018). This yields new positive results for specific functions in the former model, as well as a simple unifying barrier for obtaining negative results in both models

On Fully Secure MPC with Solitary Output

We study the possibility of achieving full security, with guaranteed output delivery, for secure multiparty computation of functionalities where only one party receives output, to which we refer as solitary functionalities. In the standard setting where all parties receive an output, full security typically requires an honest majority; otherwise even just achieving fairness is impossible. However, for solitary functionalities, fairness is clearly not an issue. This raises the following question: Is full security with no honest majority possible for all solitary functionalities? We give a negative answer to this question, by showing the existence of solitary functionalities that cannot be computed with full security. While such a result cannot be proved using fairness based arguments, our proof builds on the classical proof technique of Cleve (STOC 1986) for ruling out fair coin-tossing and extends it in a nontrivial way. On the positive side, we show that full security against any number of malicious parties is achievable for many natural and useful solitary functionalities, including ones for which the multi-output version cannot be realized with full security

The Go-GN Open Research Handbook

This Handbook draws together work done between 2020 and 2023 by members of the Global OER Graduate Network (GO-GN). GO-GN is a network of PhD candidates around the world whose research projects include a focus on open education. GO-GN is currently funded through the OER programme of The William and Flora Hewlett Foundation and administered by the Open Education Research Hub from the Institute of Educational Technology at The Open University, UK. In our current phase of activity, we began these collaborative writing efforts with a Research Methods Handbook which was created during the depths of the Covid-19 pandemic. Working together at distance provided an important way to strengthen community links when meeting in person was not possible. The Research Methods Handbook was well received by a much larger audience than we anticipated, and went on to win an Open Research Award. We followed this up with a sister publication, our Conceptual Frameworks Guide. This explores a less well traversed (but nonetheless important) area of scholarly focus. Together, these two explore open approaches to the theory and practice of research in open education. One distinctive feature of our presentation is to foreground the authentic experiences of doctoral researchers who have used specific approaches in researching open education. While it is not possible to cover all approaches in this detail, we hope that important insights are presented in this form of open practice. Throughout 2020-2022 we also regularly engaged our membership through collective reviews of recently published papers and articles. The Research Reviews serve as an overview of recent research but also as a snapshot of the critical responses recorded by doctoral and post-doctoral researchers working in relevant areas. No one volume can claim to comprehensively contain the diversity and variety of open approaches, and this is no exception. But one virtue of openness is that we can draw on the openly licensed works of others to increase our coverage of relevant areas. The Additional Resources at the end of this volume bring together a range of openly licensed texts on open education research and suggests places for further reading and research. Consequently, the information contained here represents a wide range of contributors and collaborators. The original and intended audience for this volume is the doctoral student working on an open education research project - in short, the typical student member of GO-GN and the profile the network exists to support. However, we’ve learned through feedback and analytics that the potential audience for works like this is much larger. Many people who wouldn’t describe themselves as researchers still do research and evaluation. Presenting accessible insights into research foundations and practices helps with this and can be understood as a form of open practice

On Lotteries with Unique Winners

Lotteries with the unique maximum property and the unique winner property are considered. Tight lower bounds are proven on the domain size of such lotteries. 1 Introduction A lottery is a collection of discrete, independent random variables \Pi 1 ; : : : ; \Pi N defined over a set f1; : : : ; Bg. We associate with the random variable \Pi i a player P i . A lottery has the unique maximum property if for every subset of \Pi 1 ; : : : ; \Pi N , with constant probability (say 2=3), the maximum value of the random variables is chosen by exactly one random variable. (Formally, for every non-empty subset S ` f1; : : : ; Ng, define the random variable M S = max fi2Sg \Pi i . Let p S be the probability that jfi 2 S : \Pi i = M S gj = 1. The unique maximum property states that p S 2=3 for every S.) A lottery has the unique winner property if for every subset of random variables, with constant probability, there exists a value that is chosen by exactly one random variable. (Formally, let q..

An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs

Abstract This research revealed the antecedes of two learner-centered outcome measures of success in massive open online courses (MOOCs): learner satisfaction and learner intention-fulfillment. Previous studies used success criteria from formal education contexts placing retention and completion rates as the ultimate outcome measures. We argue that the suggested learner-centered outcomes are more appropriate for measuring success in non-formal lifelong learning settings because they are focused on the learner’s intentions, rather than the intentions of the course developer. The behavioural measures of 125 MOOC participants who answered a pre- and a post-questionnaire were harvested. The analysis revealed that learner satisfaction was directly affected by: the importance of the MOOC’s benefits; online self-regulated learning - goal setting; number of video lectures accessed; and, perceived course usability. Age and the number of quizzes accessed indirectly effected learner satisfaction, through perceived course usability and through number of video lectures accessed. Intention-fulfillment was directly affected by: gender; the importance of the MOOC’s benefits; online self-regulated learning - goal setting; the number of quizzes accessed; the duration of participation; and, perceived course usability. Previous experience with MOOCs and the importance of MOOC’s benefits, indirectly affected intention-fulfillment through the number of quizzes accessed and perceived course usability

What are the barriers to learners’ satisfaction in MOOCs and what predicts them?:The role of age, intention, self-regulation, self-efficacy and motivation

Massive open online course (MOOC) participants face diverse barriers that prevent them from feeling satisfied with participating in online courses. This study identified those barriers and their predictors. Using pre- and post-questionnaires, MOOC participants reported several characteristics and their barriers to satisfaction during the course. Exploratory factor analysis identified three kinds of barriers. The effects of participants´ age, gender, level of self-efficacy, motivation, self-regulated learning skills and the intention to complete the course were used as predictors of those barriers to satisfaction. The barrier lack of interestingness/relevance was predicted by the self-regulation indices of self-evaluation, study-strategy and help-seeking. The barrier lack of time/bad planning was predicted by the self-regulation indices of goal setting, time management and study strategy and by the age of the respondent. The barrier lack of knowledge/technical problem was predicted by the level of self-efficacy, extrinsic motivation and the self-regulation index of time management, as well as by the behavioural intention to complete the course. Furthermore, an index averaging the extent of the barriers was predicted by the self-regulation indices of goal setting and study strategy, the level of self-efficacy and the level of extrinsic motivation. Theoretical and practical implications are discussed in order to help MOOC participants, instructors and designers to enhance learner satisfaction