Toward Construction of Efficient Privacy Preserving Reusable Garbled Circuits

2016 ◽  
pp. 81-92 ◽  
Author(s):  
Xu An Wang
Keyword(s):  
Privacy Preserving ◽  
Garbled Circuits

scholarly journals Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities

2019 ◽  
Vol 2019 ◽  
pp. 1-28 ◽  
Author(s):  
Joseph I. Choi ◽  
Kevin R. B. Butler
Keyword(s):  
Real Time ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Garbled Circuits ◽  
Challenges And Opportunities ◽  
Trusted Hardware ◽  
History Of

When two or more parties need to compute a common result while safeguarding their sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled circuits. The traditional enabler of SMC is cryptography, but the significant number of cryptographic operations required results in these techniques being impractical for most real-time, online computations. Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable. This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC. This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation.

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.

scholarly journals Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

2017 ◽  
Vol 2017 (4) ◽  
pp. 345-364 ◽  
Author(s):  
Adrià Gascón ◽  
Phillipp Schoppmann ◽  
Borja Balle ◽  
Mariana Raykova ◽  
Jack Doerner ◽  
...  
Keyword(s):  
Linear Regression ◽  
Convergence Rates ◽  
Comprehensive Evaluation ◽  
Linear Equations ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Training Dataset ◽  
Linear Regression Models ◽  
Garbled Circuits ◽  
Point Representation

Abstract We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

scholarly journals Securely Outsourcing ID3 Decision Tree in Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ye Li ◽  
Zoe L. Jiang ◽  
Xuan Wang ◽  
Junbin Fang ◽  
En Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Decision Tree ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security Model ◽  
Garbled Circuits ◽  
Private Data ◽  
Malicious Model ◽  
And Performance

With the wide application of Internet of Things (IoT), a huge number of data are collected from IoT networks and are required to be processed, such as data mining. Although it is popular to outsource storage and computation to cloud, it may invade privacy of participants’ information. Cryptography-based privacy-preserving data mining has been proposed to protect the privacy of participating parties’ data for this process. However, it is still an open problem to handle with multiparticipant’s ciphertext computation and analysis. And these algorithms rely on the semihonest security model which requires all parties to follow the protocol rules. In this paper, we address the challenge of outsourcing ID3 decision tree algorithm in the malicious model. Particularly, to securely store and compute private data, the two-participant symmetric homomorphic encryption supporting addition and multiplication is proposed. To keep from malicious behaviors of cloud computing server, the secure garbled circuits are adopted to propose the privacy-preserving weight average protocol. Security and performance are analyzed.

scholarly journals Scalable Secure Privacy-Preserving Record Linkage (PPRL) Methods Using Cloud-based Infrastructure

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Toan Ong ◽  
Ibrahim Lazrig ◽  
Indrajit Ray ◽  
Indrakshi Ray ◽  
Michael Kahn
Keyword(s):  
Parallel Processing ◽  
Record Linkage ◽  
High Capacity ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Third Party ◽  
Major Drawback ◽  
Garbled Circuits ◽  
Chunk Size ◽  
Synthetic Datasets

IntroductionBloom Filters (BFs) are a scalable solution for probabilistic privacy-preserving record linkage but BFs can be compromised. Yao’s garbled circuits (GCs) can perform secure multi-party computation to compute the similarity of two BFs without a trusted third party. The major drawback of using BFs and GCs together is poor efficiency. Objectives and ApproachWe evaluated the feasibility of BFs+GCs using high capacity compute engines and implementing a novel parallel processing framework in Google Cloud Compute Engines (GCCE). In the Yao’s two-party secure computation protocol, one party serves as the generator and the other party serves as the evaluator. To link data in parallel, records from both parties are divided into chunks. Linkage between every two chunks in the same block is processed by a thread. The number of threads for linkage depends on available computing resources. We tested the parallelized process in various scenarios with variations in hardware and software configurations. ResultsTwo synthetic datasets with 10K records were linked using BFs+GCs on 12 different software and hardware configurations which varied by: number of CPU cores (4 to 32), memory size (15GB – 28.8GB), number of threads (6-41), and chunk size (50-200 records). The minimum configuration (4 cores; 15GB memory) took 8,062.4s to complete whereas the maximum configuration (32 cores; 28.8GB memory) took 1,454.1s. Increasing the number of threads or changing the chunk size without providing more CPU cores and memory did not improve the efficiency. Efficiency is improved on average by 39.81% when the number of cores and memory on the both sides are doubled. The CPU utilization is maximized (near 100% on both sides) when the computing power of the generator is double the evaluator. Conclusion/ImplicationsThe PPRL runtime of BFs+GCs was greatly improved using parallel processing in a cloud-based infrastructure. A cluster of GCCEs could be leveraged to reduce the runtime of data linkage operations even further. Scalable cloud-based infrastructures can overcome the trade-off between security and efficiency, allowing computationally complex methods to be implemented.

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>

Privacy Preserving Public Auditing in Secured Cloud Storage

2012 ◽  
Vol 3 (3) ◽  
pp. 60-61
Author(s):  
V.Sajeev V.Sajeev ◽  
◽  
R.Gowthamani R.Gowthamani
Keyword(s):  
Cloud Storage ◽  
Privacy Preserving ◽  
Public Auditing

Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

2018 ◽  
Vol E101.A (1) ◽  
pp. 138-148 ◽  
Author(s):  
Haruna HIGO ◽  
Toshiyuki ISSHIKI ◽  
Kengo MORI ◽  
Satoshi OBANA
Keyword(s):  
Privacy Preserving ◽  
Hill Climbing
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