scholarly journals GAF: A General Auction Framework for Secure Combinatorial Auctions

2021 ◽  
Author(s):  
◽  
Wayne Thomson
Keyword(s):  
Resource Allocation ◽  
Private Information ◽  
Privacy Preserving ◽  
Combinatorial Auctions ◽  
Target Domain ◽  
Grid Resource ◽  
Grid Security ◽  
Network Bandwidth ◽  
Garbled Circuits ◽  
Resource Allocation Systems

<p>Auctions are an economic mechanism for allocating goods to interested parties. There are many methods, each of which is an Auction Protocol. Some protocols are relatively simple such as English and Dutch auctions, but there are also more complicated auctions, for example combinatorial auctions which sell multiple goods at a time, and secure auctions which incorporate security solutions. Corresponding to the large number of protocols, there is a variety of purposes for which protocols are used. Each protocol has different properties and they differ between how applicable they are to a particular domain.  In this thesis, the protocols explored are privacy preserving secure combinatorial auctions which are particularly well suited to our target domain of computational grid system resource allocation. In grid resource allocation systems, goods are best sold in sets as bidders value different sets of goods differently. For example, when purchasing CPU cycles, memory is also required but a bidder may additionally require network bandwidth. In untrusted distributed systems such as a publicly accessible grid, security properties are paramount. The type of secure combinatorial auction protocols explored in this thesis are privacy preserving protocols which hide the bid values of losing bidder’s bids. These protocols allow bidders to place bids without fear of private information being leaked.  With the large number of permutations of different protocols and configurations, it is difficult to manage the idiosyncrasies of many different protocol implementations within an individual application. This thesis proposes a specification, design, and implementation for a General Auction Framework (GAF). GAF provides a consistent method of implementing different types of auction protocols from the standard English auction through to the more complicated combinatorial and secure auctions. The benefit of using GAF is the ability to easily leverage multiple protocols within a single application due to the consistent specification of protocol construction.  The framework has be tested with three different protocols: the Secure Polynomial auction protocol, the Secure Homomorphic auction protocol and the Secure Garbled Circuits auction protocol. These three protocols and a statistics collecting application is a proof of concept for the framework and provides the beginning of an analysis designed at determining suitable protocol candidates for grid systems.</p>

scholarly journals GAF: A General Auction Framework for Secure Combinatorial Auctions

2021 ◽  
Author(s):  
◽  
Wayne Thomson
Keyword(s):  
Resource Allocation ◽  
Private Information ◽  
Privacy Preserving ◽  
Combinatorial Auctions ◽  
Target Domain ◽  
Grid Resource ◽  
Grid Security ◽  
Network Bandwidth ◽  
Garbled Circuits ◽  
Resource Allocation Systems

<p>Auctions are an economic mechanism for allocating goods to interested parties. There are many methods, each of which is an Auction Protocol. Some protocols are relatively simple such as English and Dutch auctions, but there are also more complicated auctions, for example combinatorial auctions which sell multiple goods at a time, and secure auctions which incorporate security solutions. Corresponding to the large number of protocols, there is a variety of purposes for which protocols are used. Each protocol has different properties and they differ between how applicable they are to a particular domain.  In this thesis, the protocols explored are privacy preserving secure combinatorial auctions which are particularly well suited to our target domain of computational grid system resource allocation. In grid resource allocation systems, goods are best sold in sets as bidders value different sets of goods differently. For example, when purchasing CPU cycles, memory is also required but a bidder may additionally require network bandwidth. In untrusted distributed systems such as a publicly accessible grid, security properties are paramount. The type of secure combinatorial auction protocols explored in this thesis are privacy preserving protocols which hide the bid values of losing bidder’s bids. These protocols allow bidders to place bids without fear of private information being leaked.  With the large number of permutations of different protocols and configurations, it is difficult to manage the idiosyncrasies of many different protocol implementations within an individual application. This thesis proposes a specification, design, and implementation for a General Auction Framework (GAF). GAF provides a consistent method of implementing different types of auction protocols from the standard English auction through to the more complicated combinatorial and secure auctions. The benefit of using GAF is the ability to easily leverage multiple protocols within a single application due to the consistent specification of protocol construction.  The framework has be tested with three different protocols: the Secure Polynomial auction protocol, the Secure Homomorphic auction protocol and the Secure Garbled Circuits auction protocol. These three protocols and a statistics collecting application is a proof of concept for the framework and provides the beginning of an analysis designed at determining suitable protocol candidates for grid systems.</p>

scholarly journals Techniques and Challenges while Applying Machine Learning Algorithms in Privacy Preserving Fashion

2020 ◽  
Vol 3 ◽  
pp. xix-xix
Author(s):  
Artrim Kjamilji
Keyword(s):  
Machine Learning ◽  
Private Information ◽  
Cyber Security ◽  
Credit Card ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Machine Learning Algorithms ◽  
Garbled Circuits ◽  
Private Data

Nowadays many different entities collect data of the same nature, but in slightly different environments. In this sense different hospitals collect data about their patients’ symptoms and corresponding disease diagnoses, different banks collect transactions of their customers’ bank accounts, multiple cyber-security companies collect data about log files and corresponding attacks, etc. It is shown that if those different entities would merge their privately collected data in a single dataset and use it to train a machine learning (ML) model, they often end up with a trained model that outperforms the human experts of the corresponding fields in terms of accurate predictions. However, there is a drawback. Due to privacy concerns, empowered by laws and ethical reasons, no entity is willing to share with others their privately collected data. The same problem appears during the classification case over an already trained ML model. On one hand, a user that has an unclassified query (record), doesn’t want to share with the server that owns the trained model neither the content of the query (which might contain private data such as credit card number, IP address, etc.), nor the final prediction (classification) of the query. On the other hand, the owner of the trained model doesn’t want to leak any parameter of the trained model to the user. In order to overcome those shortcomings, several cryptographic and probabilistic techniques have been proposed during the last few years to enable both privacy preserving training and privacy preserving classification schemes. Some of them include anonymization and k-anonymity, differential privacy, secure multiparty computation (MPC), federated learning, Private Information Retrieval (PIR), Oblivious Transfer (OT), garbled circuits and/or homomorphic encryption, to name a few. Theoretical analyses and experimental results show that the current privacy preserving schemes are suitable for real-case deployment, while the accuracy of most of them differ little or not at all with the schemes that work in non-privacy preserving fashion.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

2017 ◽  
Vol 26 (1) ◽  
pp. 169-184 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Nneoma A. Okoroafor ◽  
Seyed M. Buhari ◽  
Marc E. Frincu ◽  
Sahalu B. Junaidu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Grid Computing ◽  
Resource Selection ◽  
Computing System ◽  
Population Based ◽  
Fine Tuning ◽  
Grid Resource ◽  
Performance Results ◽  
Grid Resources

AbstractThe operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

Grid Resource Allocation

2009 ◽  
pp. 171-190
Author(s):  
Zhou Lei ◽  
Zhifeng Yun ◽  
Gabrielle Allen
Keyword(s):  
Resource Allocation ◽  
Grid Resource ◽  
Grid Resource Allocation
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