Modular control under privacy protection: Fundamental trade-offs

With the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.

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Learning With Differential Privacy

Handbook of Research on Cyber Crime and Information Privacy - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-5728-0.ch019 ◽

2021 ◽

pp. 372-395

Author(s):

Poushali Sengupta ◽

Sudipta Paul ◽

Subhankar Mishra

Keyword(s):

Privacy Protection ◽

Differential Privacy ◽

Intrusion Detection Systems ◽

Sensitive Information ◽

Detection Systems ◽

Trade Offs ◽

Personal Level ◽

Encryption Decryption ◽

Individual Trees ◽

Prevention Methods

The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.

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Data Privacy

10.1093/acprof:oso/9780190657932.003.0008 ◽

2017 ◽

Author(s):

Peter F. Cowhey ◽

Jonathan D. Aronson

Keyword(s):

Privacy Protection ◽

Data Privacy ◽

Economic Cooperation ◽

International Agreements ◽

Asia Pacific ◽

The Political ◽

Trade Offs ◽

Privacy Risks ◽

Political Economic ◽

The U.S

Chapter 8 lays out the political economic trade-offs in privacy protection designs and their implications for the types of privacy risks and constraints on innovations. To delve more deeply, it then contrasts the U.S. and EU approaches. This leads into an analysis of the protracted U.S.–EU disputes on privacy safeguards and the efforts to forge international agreements on privacy protection forged at the Organization for Economic Cooperation and Development and the Asia-Pacific Economic Cooperation. The most promising initiatives will require a significant role for global civil society in governance. The three issues examined in Part III are interlinked. A robust trade regime for the cloud ecosystem requires that common international understandings about cybersecurity and digital privacy also be developed. However, tidy grand bargains are unnecessary to make progress on these linked issues.

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View analysis of personal information leakage and privacy protection in big data era—based on Q method

Aslib Journal of Information Management ◽

10.1108/ajim-05-2021-0144 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Lei Huang ◽

Jingyi Zhou ◽

Jiecong Lin ◽

Shengli Deng

Keyword(s):

Big Data ◽

Privacy Protection ◽

Personal Information ◽

Information Leakage ◽

Q Method ◽

Information Protection ◽

Content Type ◽

Trade Offs ◽

Big Data Technology ◽

Personal Information Protection

PurposeIn the era of big data, people are more likely to pay attention to privacy protection with facing the risk of personal information leakage while enjoying the convenience brought by big data technology. Furthermore, people’s views on personal information leakage and privacy protection are varied, playing an important role in the legal process of personal information protection. Therefore, this paper aims to propose a semi-qualitative method based framework to reveal the subjective patterns about information leakage and privacy protection and further provide practical implications for interested party.Design/methodology/approachQ method is a semi-qualitative methodology which is designed for identifying typologies of perspectives. In order to have a comprehensive understanding of users’ viewpoints, this study incorporates LDA & TextRank method and other information extraction technologies to capture the statements from large-scale literature, app reviews, typical cases and survey interviews, which could be regarded as the resource of the viewpoints.FindingsBy adopting the Q method that aims for studying subjective thought patterns to identify users’ potential views, the authors have identified three categories of stakeholders’ subjectivities: macro-policy sensitive, trade-offs and personal information sensitive, each of which perceives different risk and affordance of information leakage and importance and urgency of privacy protection. All of the subjectivities of the respondents reflect the awareness of the issue of information leakage, that is, the interested parties like social network sites are unable to protect their full personal information, while reflecting varied resistance and susceptibility of disclosing personal information for big data technology applications.Originality/valueThe findings of this study provide an overview of the subjective patterns on the information leakage issue. Being the first to incorporate the Q method to study the views of personal information leakage and privacy protection, the research not only broadens the application field of the Q method but also enriches the research methods for personal information protection. Besides, the proposed LDA & TextRank method in this paper alleviates the limitation of statements resource in the Q method.

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Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01602-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Felix Nikolaus Wirth ◽

Thierry Meurers ◽

Marco Johns ◽

Fabian Prasser

Keyword(s):

Medical Research ◽

Data Sharing ◽

Privacy Protection ◽

Data Privacy ◽

Privacy Preserving ◽

Distributed Data ◽

Research Gaps ◽

Individual Level ◽

Shared Data ◽

Trade Offs

Abstract Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

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Novel players fine-tune plant trade-offs

Essays in Biochemistry ◽

10.1042/bse0580083 ◽

2015 ◽

Vol 58 ◽

pp. 83-100 ◽

Cited By ~ 25

Author(s):

Selena Gimenez-Ibanez ◽

Marta Boter ◽

Roberto Solano

Keyword(s):

Ubiquitin Ligase ◽

Feedback Loop ◽

Molecular Level ◽

26S Proteasome ◽

Signalling Molecules ◽

Transcriptional Reprogramming ◽

Trade Offs ◽

Jaz Proteins ◽

Regulatory Feedback Loop ◽

Fine Tune

Jasmonates (JAs) are essential signalling molecules that co-ordinate the plant response to biotic and abiotic challenges, as well as co-ordinating several developmental processes. Huge progress has been made over the last decade in understanding the components and mechanisms that govern JA perception and signalling. The bioactive form of the hormone, (+)-7-iso-jasmonyl-l-isoleucine (JA-Ile), is perceived by the COI1–JAZ co-receptor complex. JASMONATE ZIM DOMAIN (JAZ) proteins also act as direct repressors of transcriptional activators such as MYC2. In the emerging picture of JA-Ile perception and signalling, COI1 operates as an E3 ubiquitin ligase that upon binding of JA-Ile targets JAZ repressors for degradation by the 26S proteasome, thereby derepressing transcription factors such as MYC2, which in turn activate JA-Ile-dependent transcriptional reprogramming. It is noteworthy that MYCs and different spliced variants of the JAZ proteins are involved in a negative regulatory feedback loop, which suggests a model that rapidly turns the transcriptional JA-Ile responses on and off and thereby avoids a detrimental overactivation of the pathway. This chapter highlights the most recent advances in our understanding of JA-Ile signalling, focusing on the latest repertoire of new targets of JAZ proteins to control different sets of JA-Ile-mediated responses, novel mechanisms of negative regulation of JA-Ile signalling, and hormonal cross-talk at the molecular level that ultimately determines plant adaptability and survival.

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Proposal Tightens Privacy Protection

Internal Medicine News ◽

10.1016/s1097-8690(10)70711-3 ◽

2010 ◽

Vol 43 (13) ◽

pp. 77

Author(s):

MARY ELLEN SCHNEIDER

Keyword(s):

Privacy Protection

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Dual Goals for Speed and Accuracy on the Same Performance Task

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000063 ◽

2012 ◽

Vol 11 (3) ◽

pp. 118-126 ◽

Cited By ~ 3

Author(s):

Olive Emil Wetter ◽

Jürgen Wegge ◽

Klaus Jonas ◽

Klaus-Helmut Schmidt

Keyword(s):

Memory Scanning ◽

Performance Task ◽

Performance Tasks ◽

Trade Off ◽

Test Experiment ◽

Trade Offs ◽

New Finding ◽

Sternberg Paradigm ◽

Speed Accuracy ◽

Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

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