2,766 research outputs found
Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid
As the ease with which any data are collected and transmitted increases,
more privacy concerns arise leading to an increasing need to protect and preserve
it. Much of the recent high-profile coverage of data mishandling and public mis-
leadings about various aspects of privacy exasperates the severity. The Smart Grid
(SG) is no exception with its key characteristics aimed at supporting bi-directional
information flow between the consumer of electricity and the utility provider. What
makes the SG privacy even more challenging and intriguing is the fact that the very
success of the initiative depends on the expanded data generation, sharing, and pro-
cessing. In particular, the deployment of smart meters whereby energy consumption
information can easily be collected leads to major public hesitations about the tech-
nology. Thus, to successfully transition from the traditional Power Grid to the SG
of the future, public concerns about their privacy must be explicitly addressed and
fears must be allayed. Along these lines, this chapter introduces some of the privacy
issues and problems in the domain of the SG, develops a unique taxonomy of some
of the recently proposed privacy protecting solutions as well as some if the future
privacy challenges that must be addressed in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd
Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review
IoT data markets in public and private institutions have become increasingly
relevant in recent years because of their potential to improve data
availability and unlock new business models. However, exchanging data in
markets bears considerable challenges related to disclosing sensitive
information. Despite considerable research focused on different aspects of
privacy-enhancing data markets for the IoT, none of the solutions proposed so
far seems to find a practical adoption. Thus, this study aims to organize the
state-of-the-art solutions, analyze and scope the technologies that have been
suggested in this context, and structure the remaining challenges to determine
areas where future research is required. To accomplish this goal, we conducted
a systematic literature review on privacy enhancement in data markets for the
IoT, covering 50 publications dated up to July 2020, and provided updates with
24 publications dated up to May 2022. Our results indicate that most research
in this area has emerged only recently, and no IoT data market architecture has
established itself as canonical. Existing solutions frequently lack the
required combination of anonymization and secure computation technologies.
Furthermore, there is no consensus on the appropriate use of blockchain
technology for IoT data markets and a low degree of leveraging existing
libraries or reusing generic data market architectures. We also identified
significant challenges remaining, such as the copy problem and the recursive
enforcement problem that-while solutions have been suggested to some extent-are
often not sufficiently addressed in proposed designs. We conclude that
privacy-enhancing technologies need further improvements to positively impact
data markets so that, ultimately, the value of data is preserved through data
scarcity and users' privacy and businesses-critical information are protected.Comment: 49 pages, 17 figures, 11 table
Functional encryption based approaches for practical privacy-preserving machine learning
Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical.
In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient
- …