Abstract

The emergence of e-commerce and e-voting platforms has resulted in the rise in the volume of sensitive information over the Internet. This has resulted in an increased demand for secure and private means of information computation. Towards this, the Yao's Millionaires' problem, ie, to determine the richer among two millionaires' securely, finds an application. In this work, we present a new solution to the Yao's Millionaires' problem namely, Privacy Preserving Comparison (PPC). We show that PPC achieves this comparison in constant time as well as in one execution. PPC uses semi-honest third parties for the comparison who do not learn any information about the values. Further, we show that PPC is collusion-resistance. To demonstrate the significance of PPC, we present a secure, approximate single-minded combinatorial auction, which we call TPACAS, ie, Truthful, Privacy-preserving Approximate Combinatorial Auction for Single-minded bidders. We show that TPACAS, unlike previous works, preserves the following privacies relevant to an auction: agent privacy, the identities of the losing bidders must not be revealed to any other agent except the auctioneer (AU), bid privacy, the bid values must be hidden from the other agents as well as the AU and bid-topology privacy, the items for which the agents are bidding must be hidden from the other agents as well as the AU. We demonstrate the practicality of TPACAS through simulations. Lastly, we also look at TPACAS'implementation over a publicly distributed ledger, such as the Ethereum blockchain.

Abstract

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduceemph {selective} andemph {cumulative} fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM usestextit {(i)} aemph {report strength score} to remove the risk of random pairing with dishonest reporters,textit {(ii)} aemph {consistency score} to measure an agent's history of accurate reports and distinguish valuable reports,textit {(iii)} aemph {reliability score} to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, andtextit {(iv)} aemph {location robustness score} to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.

Abstract

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters,(ii) a consistency score to measure an agent’s history of accurate reports and distinguish valuable reports,(iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.

Abstract

In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, ie, mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.

Abstract

Auctions are mechanisms which facilitate the buying and selling of goods/items amongst a group of agents. In general, a combinatorial auction, where the agents can bid for combination (s) of items, yields a higher revenue than selling the goods/items individually.

Abstract

We focus on the aggregation of citizen preferences for public projects through civic crowdfunding. Existing civic crowdfunding mechanisms consider only agents with positive valuation towards the public project. Moreover, these mechanisms assume that each agent has a symmetric belief about the project getting provisioned. As public projects aim to cater to the majority, they should be provisioned only if the majority prefers it. To incorporate negative valuations, we propose a methodology to convert existing civic crowdfunding mechanisms for positive preferences to cater to markets having both types of agents. Specifically, we adapt existing PPR and PPS mechanisms to design PPRN and PPSN, that incentivize agents to contribute towards or against the project’s provision: based on their preference. Besides, to address asymmetric beliefs, we propose a novel reward scheme, Belief Based Reward (BBR) based on Robust Bayesian Truth Serum (RBTS) mechanism. BBR rewards agents based on their belief towards the project’s provision. Using this reward scheme, we introduce a general mechanism for civic crowdfunding which allows for agents having asymmetric beliefs towards the project getting provisioned and incentivizes them to contribute towards the project’s provision. We illustrate the general mechanism by designing two novel mechanisms, namely PPRx and PPSx, adapting PPR and PPS respectively, and prove that in both the mechanisms the project is provisioned at equilibrium

Abstract

In many practical applications such as crowd-sourcing and online advertisement, use of mechanism design (auction-based mechanisms) depends upon inherent stochastic parameters which are unknown. These parameters are learnt using multi-armed bandit (MAB) algorithms. The mechanisms which incorporate MAB are referred to as Multi-Armed-Bandit Mechanisms. While most of the MAB mechanisms focus on frequentist approaches like upper confidence bound algorithms, recent work has shown that using Bayesian approaches like Thompson sampling results in mechanisms with better regret bounds; although lower regret is obtained at the cost of the mechanism ending up with a weaker game theoretic property ie Within-Period Dominant Strategy Incentive Compatibility (WP-DSIC). The existing payment rules used in the Thompson sampling based mechanisms may cause negative utility to the auctioneer. In addition, if we wish to minimize the cost to the auctioneer, it is very challenging to design payment rules that satisfy WP-DSIC while learning through Thompson sampling. In our work, we propose a data-driven approach for designing MAB-mechanisms. Specifically, we use neural networks for designing the payment rule which is WP-DSIC, while the allocation rule is modeled using Thompson sampling. Our results, in the setting of crowd-sourcing for recruiting quality workers, indicate that the learned payment rule guarantees better cost while maximizing the social welfare and also ensuring reduced variance in the utilities to the agents.

Abstract

Methods and systems are disclosed for providing cloud services to multiple customers in a cloud. One embodiment includes receiving a number of requests for the cloud services from the multiple customers simultaneously or substantially simultaneously; prioritizing the requests based on a probability distribution of actually deploying a service, a budget of the customers, and an expected demand of the requested service based on the probability distribution; generating a number of cloud configurations along with a number of Service Level Agreements (SLAs) for the customers based on prioritization of the requests, a class & past behavior of the customers, and a current demand of the cloud services, the SLAs of the customers include differentiated price offering; recommending the cloud configurations and the SLAs to the customers; allowing the customers to negotiate terms of the SLAs; and providing the cloud …

Abstract

We describe the design of an autonomous electricity broker agent, VidyutVanika, the runner-up of the 2018 PowerTAC competition. The agent uses techniques from reinforcement learning, dynamic programming and other areas of machine learning to seek appropriate actions in tariff and wholesale market of the PowerTAC simulation environment. The novelty of our agent lies in defining the reward functions of suitably defined Markov decision processes (MDPs), solving these MDPs, and applying these solutions to real actions in the market. In addition, VidyutVanika uses a neural network to predict the energy consumption of various customers using weather data. The usage forecasts, so obtained, are used to place orders in day-ahead wholesale market. These forecasts also helps in reducing the balancing costs incurred by the broker.

Abstract

We consider a setting where public resources are to be allocated among competing and strategic agents so as to maximize social welfare (the objects should be allocated to those who value them the most). This is called allocative efficiency (AE). We need the agents to report their valuations for obtaining these resources, truthfully referred to as dominant strategy incentive compatibility (DSIC). We use auction-based mechanisms to achieve AE and DSIC yet budget balance cannot be ensured, due to Green-Laffont Impossibility Theorem. That is, the net transfer of money cannot be zero. This problem has been addressed by designing a redistribution mechanism so as to ensure a minimum surplus of money as well as AE and DSIC. The objective could be to minimize surplus in expectation or in the worst case and these objects could be homogeneous or heterogeneous. Designing redistribution mechanisms which perform well in expectation becomes analytically challenging for heterogeneous settings. In this paper, we take a completely different, data-driven approach. We train a neural network to determine an optimal redistribution mechanism based on given settings with both the objectives, optimal in expectation and optimal in the worst case. We also propose a loss function to train a neural network to optimize worst case. We design neural networks with the underlying rebate functions being linear as well as nonlinear in terms of bids of the agents. Our networks' performances are same as the theoretical guarantees for the cases where it has been solved. We observe that a neural network based redistribution mechanism for homogeneous …