Linked data privacy

Web of Linked Data introduces common format and principles for publishing and linking data on the Web. Such a network of linked data is publicly available and easily consumable. This paper introduces a calculus for modelling networks of linked data with encoded privacy preferences.In that calculus, a network is a parallel composition of users, where each user is named and consists of data, representing the user's profile, and a process. Data is a parallel composition of triples with names (resources) as components. Associated with each name and each triple of names are their privacy protection policies, that are represented by queries. A data triple is accessible to a user if the user's data satisfies the query assigned to that triple.The main contribution of this model lies in the type system which together with the introduced query order ensures that static type-checking prevents privacy violations. We say that a network is well behaved if —access to a triple is more restrictive than access to its components and less restrictive than access to the user name it is enclosed with,—each user can completely access their own profile,—each user can update or partly delete profiles that they own (can access the whole profiles), and—each user can update the privacy preference policy of data of another profile that they own or write data to another profile only if the newly obtained profile stays fully accessible to their owner.We prove that any well-typed network is well behaved.

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Student Privacy and the Law in the Internet Age

The Oxford Handbook of U.S. Education Law ◽

10.1093/oxfordhb/9780190697402.013.30 ◽

2019 ◽

Author(s):

Leah Plunkett ◽

Urs Gasser ◽

Sandra Cortesi

Keyword(s):

Learning Environments ◽

School Environment ◽

Privacy Protection ◽

Data Privacy ◽

Digital Technologies ◽

The United States ◽

Legal Framework ◽

Privacy Law ◽

Student Privacy ◽

High Level

New types of digital technologies and new ways of using them are heavily impacting young people’s learning environments and creating intense pressure points on the “pre-digital” framework of student privacy. This chapter offers a high-level mapping of the federal legal landscape in the United States created by the “big three” federal privacy statutes—the Family Educational Rights and Privacy Act (FERPA), the Children’s Online Privacy Protection Act (COPPA), and the Protection of Pupil Rights Amendment (PPRA)—in the context of student privacy and the ongoing digital transformation of formal learning environments (“schools”). Fissures are emerging around key student privacy issues such as: what are the key data privacy risk factors as digital technologies are adopted in learning environments; which decision makers are best positioned to determine whether, when, why, and with whom students’ data should be shared outside the school environment; what types of data may be unregulated by privacy law and what additional safeguards might be required; and what role privacy law and ethics serve as we seek to bolster related values, such as equity, agency, and autonomy, to support youth and their pathways. These and similar intersections at which the current federal legal framework is ambiguous or inadequate pose challenges for key stakeholders. This chapter proposes that a “blended” governance approach, which draws from technology-based, market-based, and human-centered privacy protection and empowerment mechanisms and seeks to bolster legal safeguards that need to be strengthen in parallel, offers an essential toolkit to find creative, nimble, and effective multistakeholder solutions.

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A Lightweight Formalism for Reference Lifetimes and Borrowing in Rust

ACM Transactions on Programming Languages and Systems ◽

10.1145/3443420 ◽

2021 ◽

Vol 43 (1) ◽

pp. 1-73

Author(s):

David J. Pearce

Keyword(s):

Memory Management ◽

Programming Model ◽

Type System ◽

Control Flow ◽

Two Phase ◽

Type Checking ◽

Strong Focus ◽

Reference Implementation ◽

Two Phases ◽

Complex Features

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.

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A federated interpretable scorecard and its application in credit scoring

International Journal of Financial Engineering ◽

10.1142/s2424786321420093 ◽

2021 ◽

pp. 2142009

Author(s):

Fanglan Zheng ◽

Erihe ◽

Kun Li ◽

Jiang Tian ◽

Xiaojia Xiang

Keyword(s):

Privacy Protection ◽

Data Privacy ◽

Credit Scoring ◽

Training Session ◽

Model Performance ◽

Parameter Tuning ◽

Kolmogorov Smirnov ◽

Data Privacy Protection ◽

Tuning Process ◽

Positive Coefficients

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.

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Is There a Better Option than the Data Transfer Model to Protect Data Privacy?

University of New South Wales Law Journal ◽

10.53637/mrob8894 ◽

2019 ◽

Vol 42 (2) ◽

Author(s):

Alan Toy ◽

Gehan Gunasekara

Keyword(s):

Privacy Protection ◽

Data Privacy ◽

Data Transfer ◽

Business Environment ◽

Transfer Model ◽

Privacy Rights ◽

Choice Of Law ◽

Privacy Laws ◽

Data Privacy Protection ◽

Extraterritorial Jurisdiction

The data transfer model and the accountability model, which are the dominant models for protecting the data privacy rights of citizens, have begun to present significant difficulties in regulating the online and increasingly transnational business environment. Global organisations take advantage of forum selection clauses and choice of law clauses and attention is diverted toward the data transfer model and the accountability model as a means of data privacy protection but it is impossible to have confidence that the data privacy rights of citizens are adequately protected given well known revelations regarding surveillance and the rise of technologies such as cloud computing. But forum selection and choice of law clauses no longer have the force they once seemed to have and this opens the possibility that extraterritorial jurisdiction may provide a supplementary conceptual basis for championing data privacy in the globalised context of the Internet. This article examines the current basis for extraterritorial application of data privacy laws and suggests a test for increasing their relevance.

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Dealing with Data Privacy Protection

IS Management Handbook, 8th Edition ◽

10.1201/9781420031393.ch56 ◽

2003 ◽

Author(s):

Fritz Grupe ◽

William Kuechler ◽

Scott Sweeney

Keyword(s):

Privacy Protection ◽

Data Privacy ◽

Data Privacy Protection

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Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032059 ◽

2021 ◽

Vol 2083 (3) ◽

pp. 032059

Author(s):

Qiang Chen ◽

Meiling Deng

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Privacy Protection ◽

Data Privacy ◽

Absolute Error ◽

Average Absolute Error ◽

Regression Algorithms ◽

Hadoop Platform ◽

Logistic Regression Algorithm ◽

Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

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Social Welfare Analysis under Different Levels of Consumers’ Privacy Regulation

Journal of theoretical and applied electronic commerce research ◽

10.3390/jtaer16070161 ◽

2021 ◽

Vol 16 (7) ◽

pp. 2943-2964

Author(s):

Xudong Lin ◽

Xiaoli Huang ◽

Shuilin Liu ◽

Yulin Li ◽

Hanyang Luo ◽

...

Keyword(s):

Social Welfare ◽

Information Sharing ◽

Privacy Protection ◽

Rapid Development ◽

Specific Information ◽

Privacy Regulation ◽

Privacy Preference ◽

The Cost ◽

The Impact ◽

Different Levels

With the rapid development of information technology, digital platforms can collect, utilize, and share large amounts of specific information of consumers. However, these behaviors may endanger information security, thus causing privacy concerns among consumers. Considering the information sharing among firms, this paper constructs a two-period duopoly price competition Hotelling model, and gives insight into the impact of three different levels of privacy regulations on industry profit, consumer surplus, and social welfare. The results show that strong privacy protection does not necessarily make consumers better off, and weak privacy protection does not necessarily hurt consumers. Information sharing among firms will lead to strong competitive effects, which will prompt firms to lower the price for new customers, thus damaging the profits of firms, and making consumers’ surplus higher. The level of social welfare under different privacy regulations depends on consumers’ product-privacy preference, and the cost of information coordination among firms. With the cost of information coordination among firms increasing, it is only in areas where consumers have greater privacy preferences that social welfare may be optimal under the weak regulation.

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