Implementing Privacy Negotiations in E-Commerce

This paper examines how service providers may resolve the trade-off between their personalization efforts and users' individual privacy concerns. Finding that neither an optimized one-size-fits-all strategy, nor a market-driven specialization of providers or choices between different usage scenarios can solve the problem, we analyze how negotiation techniques can lead to efficient contracts and how they can be integrated into current technologies. The analysis includes the identification of relevant and negotiable privacy dimensions for different usage domains. Negotiations in multi-channel retailing are examined as a detailed example. Based on a formalization of the user's privacy revelation problem, we model the negotiation process as a Bayesian game where the service provider faces different types of users. Finally an extension to P3P is proposed that allows a simple expression and implementation of negotiation processes. Support for this extension has been integrated in the Mozilla browser.

Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing-intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for the vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game-based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the best-effort worker selection scheme, the proposed scheme can improve the utility of metaverse service provider and the average profit of CDC workers

Examined Lives: Informational Privacy and the Subject as Object

In the United States, proposals for informational privacy have proved enormously controversial. On a political level, such proposals threaten powerful data processing interests. On a theoretical level, data processors and other data privacy opponents argue that imposing restrictions on the collection, use, and exchange of personal data would ignore established understandings of property, limit individual freedom of choice, violate principles of rational information use, and infringe data processors\u27 freedom of speech. In this article, Professor Julie Cohen explores these theoretical challenges to informational privacy protection. She concludes that categorical arguments from property, choice, truth, and speech lack weight, and mask fundamentally political choices about the allocation of power over information, cost, and opportunity. Each debate, although couched in a rhetoric of individual liberty, effectively reduces individuals to objects of choices and trades made by others. Professor Cohen argues, instead, that the debate about data privacy protection should be grounded in an appreciation of the conditions necessary for individuals to develop and exercise autonomy in fact, and that meaningful autonomy requires a degree of freedom from monitoring, scrutiny, and categorization by others. The article concludes by calling for the design of both legal and technological tools for strong data privacy protection

Big privacy: challenges and opportunities of privacy study in the age of big data

One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land

When Mobile Blockchain Meets Edge Computing

Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data, i.e., a block, to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin

Copyright

Copyright is the branch of Intellectual Property Law that governs works of expression such as books, paintings and songs, and the expressive aspects of computer programs. Intellectual products such as these have a partially public goods character: they are largely inexhaustible and nonexcludable. Intellectual Property Law responds to inexcludability by giving producers legal rights to exclude nonpayers from certain usages of their intellectual products. The goal is to provide incentives for new production at fairly low transaction costs. However, the copyright owner will charge a price above marginal cost and this, coupled with the inexhaustibility of most copyrighted products, creates deadweight loss. Various copyright doctrines (such as the idea/expression dichotomy, the limited duration of the copyright ownership term and the doctrine of ‘fair use’) work to reduce deadweight loss and other costs within a larger structure that creates incentives. Copyright Law, unlike Patent Law, gives owners rights only against those who actually copy the work. This limitation, too, may serve to reduce both transaction costs and deadweight loss. Empirically it is unclear how successful copyright has been in creating incentives for production, reducing transaction costs and keeping deadweight costs low

Secured Data Masking Framework and Technique for Preserving Privacy in a Business Intelligence Analytics Platform

The main concept behind business intelligence (BI) is how to use integrated data across different business systems within an enterprise to make strategic decisions. It is difficult to map internal and external BI’s users to subsets of the enterprise’s data warehouse (DW), resulting that protecting the privacy of this data while maintaining its utility is a challenging task. Today, such DW systems constitute one of the most serious privacy breach threats that an enterprise might face when many internal users of different security levels have access to BI components. This thesis proposes a data masking framework (iMaskU: Identify, Map, Apply, Sign, Keep testing, Utilize) for a BI platform to protect the data at rest, preserve the data format, and maintain the data utility on-the-fly querying level. A new reversible data masking technique (COntent BAsed Data masking - COBAD) is developed as an implementation of iMaskU. The masking algorithm in COBAD is based on the statistical content of the extracted dataset, so that, the masked data cannot be linked with specific individuals or be re-identified by any means. The strength of the re-identification risk factor for the COBAD technique has been computed using a supercomputer where, three security scheme/attacking methods are considered, a) the brute force attack, needs, on average, 55 years to crack the key of each record; b) the dictionary attack, needs 231 days to crack the same key for the entire extracted dataset (containing 50,000 records), c) a data linkage attack, the re-identification risk is very low when the common linked attributes are used. The performance validation of COBAD masking technique has been conducted. A database schema of 1GB is used in TPC-H decision support benchmark. The performance evaluation for the execution time of the selected TPC-H queries presented that the COBAD speed results are much better than AES128 and 3DES encryption. Theoretical and experimental results show that the proposed solution provides a reasonable trade-off between data security and the utility of re-identified data