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About this book

This SpringerBrief mainly focuses on effective big data analytics for CPS, and addresses the privacy issues that arise on various CPS applications. The authors develop a series of privacy preserving data analytic and processing methodologies through data driven optimization based on applied cryptographic techniques and differential privacy in this brief. This brief also focuses on effectively integrating the data analysis and data privacy preservation techniques to provide the most desirable solutions for the state-of-the-art CPS with various application-specific requirements.

Cyber-physical systems (CPS) are the “next generation of engineered systems,” that integrate computation and networking capabilities to monitor and control entities in the physical world. Multiple domains of CPS typically collect huge amounts of data and rely on it for decision making, where the data may include individual or sensitive information, for e.g., smart metering, intelligent transportation, healthcare, sensor/data aggregation, crowd sensing etc. This brief assists users working in these areas and contributes to the literature by addressing data privacy concerns during collection, computation or big data analysis in these large scale systems. Data breaches result in undesirable loss of privacy for the participants and for the entire system, therefore identifying the vulnerabilities and developing tools to mitigate such concerns is crucial to build high confidence CPS.

This Springerbrief targets professors, professionals and research scientists working in Wireless Communications, Networking, Cyber-Physical Systems and Data Science. Undergraduate and graduate-level students interested in Privacy Preservation of state-of-the-art Wireless Networks and Cyber-Physical Systems will use this Springerbrief as a study guide.

Table of Contents

Frontmatter

1. Cyber-Physical Systems

Abstract
In this chapter, we first introduce the concept of the cyber physical systems (CPS) and explain the importance of privacy and security in CPS. Then, we present four different applications of CPS in the real world and briefly summarize the state-of-art research and the research challenges for the privacy preservation and big data analysis in these CPS systems. In this chapter, we provide content outlines for the rest of the book.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han

2. Preliminaries

Abstract
In this chapter, we briefly introduce the differential privacy technique and its variants to effectively protect the participants’ privacy in different CPS. The concept of differential privacy was first proposed by Dwork (Differential privacy: a survey of results. In: International conference on theory and applications of models of computation, Xi’an, 2008) which specifies that any individual has a very small influence on the (distribution of the) outcome of the computation. Differential privacy (DP) aims to exploit the statistical information without disclosure of the data providers’ privacy. Differential privacy is a formal definition of data privacy, which ensures that any sequence of output from data set (e.g., responses to queries) is “essentially” equally likely to occur, no matter any individual is present or absent (Dwork et al., Found Trends Theor Comput Sci 9(3–4):211–407, 2014; Baranov et al., Am Econ J Microecon 9(3):1–27, 2017; Jin and Zhang, Privacy-preserving crowdsourced spectrum sensing. In: Proceeding of the IEEE international conference on computer communications (INFOCOM), pp. 1–9, 2016). In this chapter, we illustrate three variants of differential privacy, centralized different privacy, distributed differential privacy and local differential privacy that are applied to various CPS applications described in the book.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han

3. Spectrum Trading with Secondary Users’ Privacy Preservation

Abstract
As described in Sect. 1.​2, spectrum trading benefits both SUs and PUs, while it poses great challenges to maximize PUs’ revenue, since SUs’ demands are uncertain and individual SU’s traffic portfolio contains private information. In this chapter, we propose a data-driven spectrum trading scheme which maximizes PUs’ revenue and preserves SUs’ demand differential privacy. Briefly, we introduce a novel network architecture consisting of the PSP, the SSP and the STED. Under the proposed architecture, PSP aggregates available spectrum from PUs, and sells the spectrum to SSP at fixed wholesale price, directly to SUs at spot price, or both. The PSP has to accurately estimate SUs’ demands. To estimate SUs’ demand, the STED exploits data-driven approach to choose sampled SUs to construct the reference distribution of SUs’ demands, and utilizes reference distribution to estimate the demand distribution of all SUs. Moreover, the STED adds noises to preserve the demand differential privacy of sampled SUs before it answers the demand estimation queries from the PSP. With the estimated SUs’ demand, we formulate the revenue maximization problem into a risk-averse optimization, develop feasible solutions, and verify its effectiveness through both theoretical proof and simulations.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han

4. Optimization for Utility Providers with Privacy Preservation of Users’ Energy Profile

Abstract
Smart meters migrate conventional electricity grid into digitally enabled SG, which is more reliable and efficient. Fine-grained energy consumption data collected by smart meters helps utility providers accurately predict users’ demands and significantly reduce power generation cost, while it imposes severe privacy risks on consumers and may discourage them from using those “espionage meters”. To enjoy the benefits of smart meter measured data without compromising the users’ privacy, in this chapter, we try to integrate DDP techniques into data-driven optimization, and propose a novel scheme that not only minimizes the cost for utility providers but also preserves the DDP of users’ energy profiles. Briefly, we add differential private noises to the users’ energy consumption data before the smart meters send it to the utility provider. Due to the uncertainty of the users’ demand distribution, the utility provider aggregates a given set of historical users’ differentially private data, estimates the users’ demands, and formulates the data-driven cost minimization based on the collected noisy data. We also develop algorithms for feasible solutions, and verify the effectiveness of the proposed scheme through simulations using the simulated energy consumption data generated from the utility company’s real data analysis.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han

5. Caching with Users’ Differential Privacy Preservation in Information-Centric Networks

Abstract
Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users’ preferable contents without disclosing the users’ privacy, in this chapter, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users’ preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han

6. Clock Auction Inspired Privacy Preservation in Colocation Data Centers

Abstract
Data centers are key participants in emergency demand response (EDR), where the grid coordinates large electricity consumers for reducing their consumption during emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers (e.g., Google), this work studies EDR in multi-tenant colocation data centers (e.g., Equinix) where servers are owned and managed by individual tenants and which are better targets of EDR. Existing EDR mechanisms incentivize tenants’ energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants’ participation in EDR. This chapter addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using the descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation through the clock auction, where operator can only know the aggregate of the tenants’ values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulating descending clock auction, in which the amount of energy/price the tenants are willing to reduce for a given price/energy to meet EDR is protected.
Miao Pan, Jingyi Wang, Sai Mounika Errapotu, Xinyue Zhang, Jiahao Ding, Zhu Han
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