17,380 research outputs found
MixEth: Efficient, Trustless Coin Mixing Service for Ethereum
Coin mixing is a prevalent privacy-enhancing technology for cryptocurrency users. In this paper, we present MixEth, which is a trustless coin mixing service for Turing-complete blockchains. MixEth does not rely on a trusted setup and is more efficient than any proposed trustless coin tumbler. It requires only 3 on-chain transactions at most per user and 1 off-chain message. It achieves strong notions of anonymity and is able to resist denial-of-service attacks. Furthermore the underlying protocol can also be used to efficiently shuffle ballots, ciphertexts in a trustless and decentralized manner
Survey and Systematization of Secure Device Pairing
Secure Device Pairing (SDP) schemes have been developed to facilitate secure
communications among smart devices, both personal mobile devices and Internet
of Things (IoT) devices. Comparison and assessment of SDP schemes is
troublesome, because each scheme makes different assumptions about out-of-band
channels and adversary models, and are driven by their particular use-cases. A
conceptual model that facilitates meaningful comparison among SDP schemes is
missing. We provide such a model. In this article, we survey and analyze a wide
range of SDP schemes that are described in the literature, including a number
that have been adopted as standards. A system model and consistent terminology
for SDP schemes are built on the foundation of this survey, which are then used
to classify existing SDP schemes into a taxonomy that, for the first time,
enables their meaningful comparison and analysis.The existing SDP schemes are
analyzed using this model, revealing common systemic security weaknesses among
the surveyed SDP schemes that should become priority areas for future SDP
research, such as improving the integration of privacy requirements into the
design of SDP schemes. Our results allow SDP scheme designers to create schemes
that are more easily comparable with one another, and to assist the prevention
of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications
Surveys & Tutorials 2017 (Volume: PP, Issue: 99
SoK: Differential Privacies
Shortly after it was first introduced in 2006, differential privacy became
the flagship data privacy definition. Since then, numerous variants and
extensions were proposed to adapt it to different scenarios and attacker
models. In this work, we propose a systematic taxonomy of these variants and
extensions. We list all data privacy definitions based on differential privacy,
and partition them into seven categories, depending on which aspect of the
original definition is modified.
These categories act like dimensions: variants from the same category cannot
be combined, but variants from different categories can be combined to form new
definitions. We also establish a partial ordering of relative strength between
these notions by summarizing existing results. Furthermore, we list which of
these definitions satisfy some desirable properties, like composition,
post-processing, and convexity by either providing a novel proof or collecting
existing ones.Comment: This is the full version of the SoK paper with the same title,
accepted at PETS (Privacy Enhancing Technologies Symposium) 202
Siri, Alexa, and Other Digital Assistants: A Study of Customer Satisfaction With Artificial Intelligence Applications
Siri, Alexa, and other digital assistants are rapidly becoming embraced by consumers and the adoption is projected to grow from 390 million to 1.8 billion for the period of 2015 to 2021. Digital assistants are offering benefits to consumers while also proving to be a disruptive technology for businesses. Coupling digital assistants with other artificial intelligence technologies offers the potential to transform companies by creating more efficient business processes, automating complex tasks, and improving the customer service experience. Businesses have begun integrating this technology into their operations with the expectation of achieving significant productivity gains. Customer satisfaction has been discussed extensively throughout marketing literature. Yet, there is little empirical evidence of customer satisfaction with digital assistants. This study used PLS-SEM to analyze 244 survey responses obtained from a cross-section of consumers. Using the Expectations Confirmation Theory as its foundation, the study identified that expectations and confirmation of expectations substantially explained customer satisfaction with digital assistants. For practice, the study provides guidance which allows firms to prioritize marketing and managerial activities. Firms should focus priorities on assisting digital assistant users to become aware of new skill capabilities while also providing relevant examples of how these skills can be used to meet user needs. In addition, priorities should be focused on assisting users with understanding how the average person can use digital assistants to perform more than just mundane tasks with relative ease. These priorities were identified as areas of high importance for customer satisfaction and require performance improvements
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