scholarly journals SoK: Privacy-Preserving Computation Techniques for Deep Learning

2021 ◽  
Vol 2021 (4) ◽  
pp. 139-162
Author(s):  
José Cabrero-Holgueras ◽  
Sergio Pastrana
Keyword(s):  
Deep Learning ◽  
Data Science ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Third Party ◽  
Sensitive Information ◽  
Computation Techniques ◽  
Practical Applications ◽  
Advantages And Disadvantages

Abstract Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.

scholarly journals Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3221 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Stefanie Kuenzel ◽  
José-Rodrigo Córdoba-Pachón ◽  
Chris Watkins
Keyword(s):  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Value Added ◽  
Privacy Preserving ◽  
Third Party ◽  
Sensitive Information ◽  
Central Processor ◽  
Smart Metering ◽  
Trade Off ◽  
Level Data

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements.

Security and Privacy Challenges of Deep Learning

2020 ◽  
pp. 42-64
Author(s):  
J. Andrew Onesimu ◽  
Karthikeyan J. ◽  
D. Samuel Joshua Viswas ◽  
Robin D Sebastian
Keyword(s):  
Deep Learning ◽  
Differential Privacy ◽  
Homomorphic Encryption ◽  
Detailed Comparison ◽  
Privacy Preserving ◽  
Research Field ◽  
Security And Privacy ◽  
Security Attacks ◽  
Privacy Breaches ◽  
Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

Mini-batch Least-Squares Reverse Time Migration In A Deep Learning Framework

Geophysics ◽  
2020 ◽  
pp. 1-61
Author(s):  
Janaki Vamaraju ◽  
Jeremy Vila ◽  
Mauricio Araya-Polo ◽  
Debanjan Datta ◽  
Mohamed Sidahmed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Least Squares ◽  
Spatial Resolution ◽  
Data Science ◽  
Computational Cost ◽  
Computation Time ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Learning Framework ◽  
Time Migration

Migration techniques are an integral part of seismic imaging workflows. Least-squares reverse time migration (LSRTM) overcomes some of the shortcomings of conventional migration algorithms by compensating for illumination and removing sampling artifacts to increase spatial resolution. However, the computational cost associated with iterative LSRTM is high and convergence can be slow in complex media. We implement pre-stack LSRTM in a deep learning framework and adopt strategies from the data science domain to accelerate convergence. The proposed hybrid framework leverages the existing physics-based models and machine learning optimizers to achieve better and cheaper solutions. Using a time-domain formulation, we show that mini-batch gradients can reduce the computation cost by using a subset of total shots for each iteration. Mini-batch approach does not only reduce source cross-talk but also is less memory intensive. Combining mini-batch gradients with deep learning optimizers and loss functions can improve the efficiency of LSRTM. Deep learning optimizers such as the adaptive moment estimation are generally well suited for noisy and sparse data. We compare different optimizers and demonstrate their efficacy in mitigating migration artifacts. To accelerate the inversion, we adopt the regularised Huber loss function in conjunction. We apply these techniques to 2D Marmousi and 3D SEG/EAGE salt models and show improvements over conventional LSRTM baselines. The proposed approach achieves higher spatial resolution in less computation time measured by various qualitative and quantitative evaluation metrics.

scholarly journals A Cloud Based Secure Voting System using Homomorphic Encryption for Android Platform

2016 ◽  
Vol 6 (6) ◽  
pp. 2994
Author(s):  
Manish Ranjan ◽  
Ayub Hussain Mondal ◽  
Monjul Saikia
Keyword(s):  
Service Provider ◽  
Cloud Storage ◽  
Homomorphic Encryption ◽  
Information Leakage ◽  
Third Party ◽  
Sensitive Information ◽  
Cipher Text ◽  
Cloud Storage Service ◽  
Android Platform ◽  
Voting System

<p>Cloud based service provider are at its top of its services for various applications, as their services are very much reachable from anywhere anytime in current days. It is responsibility of the company that the Cloud storage is owned and maintained by themselves keeping the data available and accessible, and the physical environment protected and running. Could storage provider seem to be uncertain of confidentiality in many cases, as we need to limit ourselves on trust to a third party. Keeping our sensitive data ready to access any time anywhere with preventing any information leakage is a challenging task. Cryptography in this scenario plays an important role, providing security for information to protect valuable information resources on intranets, Internet and the cloud. In addition, Homomorphic cryptosystem is a form of Cryptography where some specific computation can be performed over the cipher text producing a resultant cipher text which, when decrypted, equals the result of operations carry out on the plaintext. With help of this unique property of homomorphism cryptography we proposed a system to keep sensitive information in encrypted form in the cloud storage/service provider and used those data as whenever we require. The scheme proposed here is designed for a secure online voting system on Android platform and voted information is encrypted and stored those in the cloud.</p>

scholarly journals Recent advances in the application of data science and machine/deep learning methods to research in chemical and biological sciences : a review

2020 ◽  
Author(s):  
Ben Geoffrey
Keyword(s):  
Biological Sciences ◽  
Deep Learning ◽  
Computational Chemistry ◽  
Data Science ◽  
De Novo ◽  
Computational Cost ◽  
Data Driven ◽  
Learning Approaches ◽  
Learning Methods ◽  
Space Data

The rise in application of methods of data science and machine/deep learning in chemical and biological sciences must be discussed in the light of the fore-running disciplines of bio/chem-informatics and computational chemistry and biology which helped in the accumulation ofenormous research data because of which successful application of data-driven approaches have been made possible now. Many of the tasks and goals of Ab initio methods in computational chemistry such as determination of optimized structure and other molecular properties of atoms, molecules, and compounds are being carried out with much lesser computational cost with data-driven machine/deep learning-based predictions. One observes a similar trend in computational biology, wherein, data-driven machine/deep learning methods are being proposed to predict the structure and dynamical of interactions of biological macromolecules such as proteins and DNA over computational expensive molecular dynamics based methods. In the cheminformatics space,one sees the rise of deep neural network-based methods that have scaled traditional structure-property/structure-activity to handle big data to design new materials with desired property and drugs with required activity in deep learning-based de novo molecular design methods. In thebioinformatics space, data-driven machine/deep learning approaches to genomic and proteomic data have led to interesting applications in fields such as precision medicine, prognosis prediction, and more. Thus the success story of the application of data science, machine/deep learning, andartificial intelligence to the disciple of chem/bio-informatics, and computational chemistry and biology has been told in light of how these fore-running disciplines had created huge repositories of data for data-driven approaches to be successful in these disciplines.

scholarly journals A More Efficient Fully Homomorphic Encryption Scheme Based on GSW and DM Schemes

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xun Wang ◽  
Tao Luo ◽  
Jianfeng Li
Keyword(s):  
Error Rate ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Practical Applications ◽  
Low Level ◽  
Matrix Operations ◽  
Major Bottleneck ◽  
Overall Efficiency

Achieving both simplicity and efficiency in fully homomorphic encryption (FHE) schemes is important for practical applications. In the simple FHE scheme proposed by Ducas and Micciancio (DM), ciphertexts are refreshed after each homomorphic operation. And ciphertext refreshing has become a major bottleneck for the overall efficiency of the scheme. In this paper, we propose a more efficient FHE scheme with fewer ciphertext refreshings. Based on the DM scheme and another simple FHE scheme proposed by Gentry, Sahai, and Waters (GSW), ciphertext matrix operations and ciphertext vector additions are both applied in our scheme. Compared with the DM scheme, one more homomorphic NOT AND (NAND) operation can be performed on ciphertexts before ciphertext refreshing. Results show that, under the same security parameters, the computational cost of our scheme is obviously lower than that of GSW and DM schemes for a depth-2 binary circuit with NAND gates. And the error rate of our scheme is kept at a sufficiently low level.

Privacy-preserving policy evaluation in multi-party access control

2021 ◽  
pp. 1-38
Author(s):  
Mina Sheikhalishahi ◽  
Ischa Stork ◽  
Nicola Zannone
Keyword(s):  
Access Control ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Sensitive Information ◽  
Functional Evaluation ◽  
Collaborative Systems ◽  
New Paradigm ◽  
Access Control Policies ◽  
The Individual

Recent years have seen an increasing popularity of online collaborative systems like social networks and web-based collaboration platforms. Collaborative systems typically offer their users a digital environment in which they can work together and share resources and information. These resources and information might be sensitive and, thus, they should be protected from unauthorized accesses. Multi-party access control is emerging as a new paradigm for the protection of co-owned and co-managed resources, where the policies of all users involved in the management of a resource should be accounted for collaborative decision making. Existing approaches, however, only focus on the jointly protection of resources and do not address the protection of the individual user policies themselves, whose disclosure might leak sensitive information. In this work, we propose a privacy-preserving mechanism for the evaluation of multi-party access control policies, which preserves the confidentiality of user policies while remaining capable of making collaborative decisions. To this end, we design secure computation protocols for the evaluation of policies in protected form against an access query and realize such protocols using two privacy-preserving techniques, namely Homomorphic Encryption and Secure Functional Evaluation. We show the practical feasibility of our mechanism in terms of computation and communication costs through an experimental evaluation.

scholarly journals Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 119 ◽  
Author(s):  
Mahboob Qaosar ◽  
Asif Zaman ◽  
Md. Siddique ◽  
Annisa ◽  
Yasuhiko Morimoto
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Homomorphic Encryption ◽  
Database Systems ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Sensitive Information ◽  
Computing Environment ◽  
Skyline Query ◽  
Attribute Value

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.

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