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.

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-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2452
Author(s):  
Faiza Loukil ◽  
Chirine Ghedira-Guegan ◽  
Khouloud Boukadi ◽  
Aïcha-Nabila Benharkat
Keyword(s):  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Aggregation Process ◽  
Smart Contract ◽  
Reasonable Choice ◽  
Iot Devices

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

Privacy-preserving data aggregation in smart metering systems: an overview

2013 ◽  
Vol 30 (2) ◽  
pp. 75-86 ◽  
Author(s):  
Z. Erkin ◽  
J. R. Troncoso-pastoriza ◽  
R. L. Lagendijk ◽  
F. Perez-Gonzalez
Keyword(s):  
Data Aggregation ◽  
Privacy Preserving ◽  
Smart Metering

Integrity and Privacy Preserving Data Aggregation Algorithm for WSNs

2014 ◽  
Vol 721 ◽  
pp. 732-735
Author(s):  
Hua Zhang
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Chinese Remainder Theorem ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Message Authentication ◽  
Authentication Codes ◽  
Integrity Checking ◽  
Data Freshness ◽  
Simulation Results

This paper proposed an integrity and privacy preserving data aggregation algorithm for WSNs, which is called IPPDA. First, it attached a group of congruent numbers to the sensing data in order to execute integrity checking operated by sink node using Chinese remainder theorem (CRT); then it computed the hash function-based message authentication codes with time and key as the parameters to satisfy data freshness; finally, it adopted a homomorphic encryption scheme to provide privacy preserving. The simulation results show that IPPDA can effectively preserve data privacy, check data integrity, satisfy data freshness, and get accurate data aggregation results while having less computation and communication cost than iCPDA and iPDA.

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

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>

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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