Abstract

We propose a new approach to improving student outcomes - the provision of the dream option that allows a student with a job offer to participate in the placement process again. Comparing three different student preference structures, we show that the dream option improves stability; however, it does not always improve student happiness and rank efficiency. Nevertheless, imposing a particular structure on student preferences, namely the sequential preference condition, improves both student happiness and rank efficiency. We recommend when to use the dream option and when not to use it. © 2022 Published by Elsevier Ltd on behalf of Indian Institute of Management Bangalore. This is an open access article under the CC BY-NC-ND license

Abstract

2022 IEEE International Conference on Blockchain (Blockchain) BLOC KVAC: A Un iversally Acceptable and Ideal Vaccination System on Blockchain Manik a Sharma Center for Securit y, Theory, and Algorithmic Research Intern ationa l Institute of Inform ation Technology, Hyderabad Gach ibowli, Hyderabad, India 500 032 manik a.sharm a @research.iiit.ac.in Kishore Kothap alli Cent er for Securit y, Theo ry, and Algorithmic Research Interna tional Institute of Information Technology, Hyderabad Gachibowli, Hyderabad , Indi a 500 032 kisho re @iiit.ac .in Sujit Gujar Machin e Learning Lab International Institute of Information Technology, Hyderabad Gachib owli, Hyderabad , India 500 032 suj it.gujar @iiit.ac.in Abstract-Vaccines are essential in pro tecting indivi dua ls and commun ities from the risk of ha rmful an d fatal diseases. The wor ld is migra ting towards the digitization of vacci nation docu- mentation. Still, the current digita l vacci nation is a centra lized mode l, lacking a nnive rsal acceptance. A Universally Acceptable an d Id eal Vacci nat ion System (UAIVS ) sho nld possess (a) a sta ndard vaccination cert ificate ver ificat ion algorithm, (h) p r i- vacy to per sonal in form a tion, (c) sca lab ilit y, (d) s npport lor all vacci nat ions, (e) acco u ntab ility for vaccination information access, and (f) a sco pe for scientific researc h a nd d evelopm en t. Blockc ha in tec hno logy int ro dnces d ecentr alizat ion, immn tability, acco unta bility, per sistence, and liveliness. T hese pro per ties ma ke block cha in an ideal can di da te to solve the existing pro blems in digital vacci nat ion. In this pa per, we prop ose a novel blockchain- base d syste m, n am ely BLO CKVAC, to implem ent a UA IVS . F urt her more , o ur wor k provi des a way to track an individ nal's an d fa mily's vacc ination histo ry, which is esse ntial for sc ient ific research a nd developm ent . BLOCKVAC is highly modular, sca l- a ble, an d cost -efficien t, pr oviding eno ugh roo m for fnt n re feature addi tio ns. The c urre nt works lack cost and sca lab ility ana lysis. WI' show thc scalability of o ur system t hro ugh a th oron gh a nalys is. O ur impl em entation shows t hat add ing one vacc inati on re cor d costs 7 . l(j ~2 ~ x 10- 6 ETH ( ~ U.U26US D) for reg istratio n, 2.5089 x 10- 5 ETH ( ~ 0.07 8USD) for vaccina tion, a nd th e syste m ca n easi ly han dl e np to 20K vaccinations per hon r. Ind ex Terms- B1ockc ha in, Eth ere um, Sm art Contrac ts, Public Acco unta bility, Sca lab ilit y I. I N TR O D UC TI O N The vaccination process, also known as imm unization , was introduced in the late 18th century by the British physician Edward Jenn er against the deadly smallpox virus [ I]. Since then. it has become an inevitab le medical proc edur e, protecting individuals and communities from harmful and fatal diseases. The World Hea lth Organi zation (WHO) estimates that vac- cination saves around 2.5 millio n child lives every year [2]. A record of vacci nation details massively aid s in providing qualit y health care to individuals [3]. Whi le secure storage and verificat ion of vaccination details are challenges even within a country, it is tediou s to merge them from across all the countries [4 ]. Moreover. a definitive process is the need of the hour to verify the required vaccinati on certification for travelers when they enter a country. We wou ld like to than k MeitY and Ripp le for fundin g this research . O ver view of existing vac cina tion mod els: Traditional paper- based vaccination records [5] are prone to challenges such as the possibil ity of losing, damaging, or forging the records without any accountability. Digital solutions replace the need for paper, provide an immutable card, and reduc e manual labor [6]. However, the current vacci nation systems follow a centralized model, where the users need to trust the cent ral autho rity. Further. these sys tems are prone to single-point failures and jeopardize data security P 1. Moreover, current vaccination portal s mainly deal with just slot bookings and issuin g non -verifiab le vaccination certificates. Mo tiva tion: Th e Covid-19 pandemic has caused a digital revolution in vaccination data managem ent. Countri es are shifting towards a digital solution to secure vaccination data ami issue certificates. However, such a system should be universally acceptabl e. We identi fy the properties requ ired for such a sys tem and call it a Universally Acceptable and Ideal Vaccination System (UA IVS). We furt her categorize the proper- ties of UAIVS into two subcategories: Universally Acceptable Vaccination System and Ideal Va ccination System. A Universally Acce ptable Vaccination System co

Abstract

We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, α-fairness, which guarantees that individuals with similar features face similar prices. First, we study discrete valuation space and give an analytical solution for optimal fair feature-based pricing. We show that the cost of fair pricing is defined as the ratio of expected revenue in optimal featurebased pricing to the expected revenue in an optimal fair feature-based pricing (COF) can be arbitrarily large in general. When the revenue function is continuous and concave with respect to the prices, we show that one can achieve COF strictly less than 2, irrespective of the model parameters. Finally, we provide an algorithm to compute fair feature-based pricing strategy that achieves this COF

Abstract

We formalize a framework for coordinating the funding of projects and sharing the costs among agents with quasi-linear utility functions and individual budgets. Our model contains the classical discrete participatory budgeting model as a special case, while capturing other well-motivated problems. We propose several important axioms and objectives and study how well they can be simultaneously satisfied. One of our main results is that whereas welfare maximization admits an FPTAS, welfare maximization subject to a well-motivated and very weak participation requirement leads to a strong inapproximability result. We show that this result is bypassed if we consider some natural restricted valuations or when we take an average-case heuristic approach.

Abstract

There is a rapid increase in the cooperative learning paradigm in online learning settings, i.e., federated learning (FL). Unlike most FL settings, there are many situations where the agents are competitive. Each agent would like to learn from others, but the part of the information it shares for others to learn from could be sensitive; thus, it desires its privacy. This work investigates a group of agents working concurrently to solve similar combinatorial bandit problems while maintaining quality constraints. Can these agents collectively learn while keeping their sensitive information confidential by employing differential privacy? We observe that communicating can reduce the regret. However, differential privacy techniques for protecting sensitive information makes the data noisy and may deteriorate than help to improve regret. Hence, we note that it is essential to decide when to communicate and what shared data to learn to strike a functional balance between regret and privacy. For such a federated combinatorial MAB setting, we propose a Privacy-preserving Federated Combinatorial Bandit algorithm, P-FCB. We illustrate the efficacy of P-FCB through simulations. We further show that our algorithm provides an improvement in terms of regret while upholding quality threshold and meaningful privacy guarantees.

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on Device-to-Device (D2D) ecosystems (e.g., crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers (DLs) to function. DLs, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing DLs are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyze Mneme, a DAG-based DL that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: 1) Proof of Context (PoC) and 2) Proof of Equivalence (PoE). PoC employs users’ context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme’s security and justify the ability of PoC and PoE to guarantee the characteristics of DLs: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely

Abstract

Electricity markets are getting more dynamic and complex today. On the one hand, there are outside-in factors like new entrants and increasing competition, regulatory mandates, and volatility of the electricity prices. In contrast, on the other hand, there are inside-out factors like the adoption of renewable energies, storages, and EV, emphasis on the generation and retail portfolio optimization together with creating affinity services around the empowered consumers. The paper will start with the introduction of this dynamic environment across the globe with a specific focus on Australia, the UK, and US electricity markets. Introduction of 5 min settlement in Australian Electricity Market, the rise of ancillary service products in the UK, or new roles like Community Choice Aggregators (CCA) in the US are some of the

Abstract

Advances in mobile computing have paved the way for new types of distributed applications that can be executed solely by mobile devices on device-to-device (D2D) ecosystems (eg, crowdsensing). Sophisticated applications, like cryptocurrencies, need distributed ledgers to function. Distributed ledgers, such as blockchains and directed acyclic graphs (DAGs), employ consensus protocols to add data in the form of blocks. However, such protocols are designed for resourceful devices that are interconnected via the Internet. Moreover, existing distributed ledgers are not deployable to D2D ecosystems since their storage needs are continuously increasing. In this work, we introduce and analyse Mneme, a DAG-based distributed ledger that can be maintained solely by mobile devices. Mneme utilizes two novel consensus protocols: Proof-of-Context (PoC) and Proof-of-Equivalence (PoE). PoC employs users' context to add data on Mneme. PoE is executed periodically to summarize data and produce equivalent blocks that require less storage. We analyze Mneme's security and justify the ability of PoC and PoE to guarantee the characteristics of distributed ledgers: persistence and liveness. Furthermore, we analyze potential attacks from malicious users and prove that the probability of a successful attack is inversely proportional to the square of the number of mobile users who maintain Mneme.

Abstract

Blockchain-based Distributed Ledgers (DLs) promise to transform the existing financial system by making it truly democratic. In the past decade, blockchain technology has seen many novel applications ranging from the banking industry to real estate. However, in order to be adopted universally, blockchain systems must be scalable to support a high volume of transactions. As we increase the throughput of the DL system, the underlying peer-to-peer network might face multiple levels of challenges to keep up with the requirements. Due to varying network capacities, the slower nodes would be at a relative disadvantage compared to the faster ones, which could negatively impact their revenue. In order to quantify their relative advantage or disadvantage, we introduce two measures of network fairness, 푝푓 , the probability of frontrunning and 훼푓 , the publishing fairness. We show that as we scale the blockchain, both these measures deteriorate, implying that the slower nodes face a disadvantage at higher throughputs. It results in the faster nodes getting more than their fair share of the reward while the slower nodes (slow in terms of network quality) get less. Thus, fairness and scalability in blockchain systems do not go hand in hand. In a setting with rational miners, lack of fairness causes miners to deviate from the “longest chain rule” or undercut, which would reduce the blockchain’s resilience against byzantine adversaries. Hence, fairness is not only a desirable property for a blockchain system but also essential for the security of the blockchain and any scalable blockchain protocol proposed must ensure fairness.

Abstract

We explore the class of problems where a central planner needs to select a subset of agents, each with different quality and cost. The planner wants to maximize its utility while ensuring that the average quality of the selected agents is above a certain threshold. When the agents’ quality is known, we formulate our problem as an integer linear program (ILP) and propose a deterministic algorithm, namely DPSS that provides an exact solution to our ILP. We then consider the setting when the qualities of the agents are unknown. We model this as a Multi-Arm Bandit (MAB) problem and propose DPSS-UCB to learn the qualities over multiple rounds. We show that after a certain number of rounds, τ , DPSS-UCB outputs a subset of agents that satisfy the average quality constraint with a high probability. Next, we provide bounds on τ and prove that after τ rounds, the algorithm incurs a regret of O(ln T), where T is the total number of rounds. We further illustrate the efficacy of DPSS-UCB through simulations. To overcome the computational limitations of DPSS, we propose a polynomial-time greedy algorithm, namely GSS, that provides an approximate solution to our ILP. We also compare the performance of DPSS and GSS through experiments.