A Literature Survey and Classifications on Data Deanonymisation

Al-Azizy, Dalal; Millard, David E.; Symeonidis, Iraklis; O’Hara, Kieron; Shadbolt, Nigel

doi:10.1007/978-3-319-31811-0_3

Cited by 13 publications

(11 citation statements)

References 52 publications

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“…This complicates sharing of any actual personal data with thirdparties who are responsible for software development and testing. Anonymization offers a partial solution to this problem; however, doing so often comes at the cost of reduced data quality [10] and proneness to deanonymization attacks [11].…”

Section: Introductionmentioning

confidence: 99%

Synthetic data generation for statistical testing

Soltana

Sabetzadeh

Briand

2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Abstract-Usage-based statistical testing employs knowledge about the actual or anticipated usage profile of the system under test for estimating system reliability. For many systems, usagebased statistical testing involves generating synthetic test data. Such data must possess the same statistical characteristics as the actual data that the system will process during operation. Synthetic test data must further satisfy any logical validity constraints that the actual data is subject to. Targeting data-intensive systems, we propose an approach for generating synthetic test data that is both statistically representative and logically valid. The approach works by first generating a data sample that meets the desired statistical characteristics, without taking into account the logical constraints. Subsequently, the approach tweaks the generated sample to fix any logical constraint violations. The tweaking process is iterative and continuously guided toward achieving the desired statistical characteristics. We report on a realistic evaluation of the approach, where we generate a synthetic population of citizens' records for testing a public administration IT system. Results suggest that our approach is scalable and capable of simultaneously fulfilling the statistical representativeness and logical validity requirements.

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Section: Introductionmentioning

confidence: 99%

Synthetic data generation for statistical testing

Soltana

Sabetzadeh

Briand

2017

2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…The collection, storage and protection of data in the D4R Challenge complies with European Union requirements regarding the protection of personal data and the protection of privacy in the electronic communications sector. Furthermore, research on the previously conducted D4D Challenges established that the data such as offered in this project does not allow identification of individuals [17,18,19,20]. Sharad and Danezis note that providing aggregated data such as antenna traffic results in "little scope of privacy breach.…”

Section: Ethical and Privacy Issuesmentioning

confidence: 99%

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Salah

Pentland

Lepri

et al. 2019

Guide to Mobile Data Analytics in Refugee Scenarios

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The Data for Refugees (D4R) Challenge is a non-profit challenge initiated to improve the conditions of the Syrian refugees in Turkey by providing a special database to scientific community for enabling research on urgent problems concerning refugees, including health, education, unemployment, safety, and social integration.The collected database is based on anonymised mobile Call Detail Record (CDR) of phone calls and SMS messages of Türk Telekom customers. It indicates broad activity and mobility patterns of refugees and citizens in Turkey for one year. The data collection period is from 1 January

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“…This de-anonymization may then leave individuals vulnerable to identity theft or other criminal acts by third persons (e.g. holding a person ransom by threatening to release potentially damaging information, such as on brain pathology) [34].…”

Section: On the Security Of Neurotechnological Devices And The Privacmentioning

confidence: 99%

Big Brain Data: On the Responsible Use of Brain Data from Clinical and Consumer-Directed Neurotechnological Devices

Kellmeyer

2018

Neuroethics

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The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of Bbig brain data^-the recording, collection and analysis of individuals' brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from unwarranted access. In this context, I then examine ways in which safeguards at the hardware and software level, as well as increasing Bdata literacy^in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the Bdatafication^in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance.

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A Literature Survey and Classifications on Data Deanonymisation

Cited by 13 publications

References 52 publications

Synthetic data generation for statistical testing

Synthetic data generation for statistical testing

Introduction to the Data for Refugees Challenge on Mobility of Syrian Refugees in Turkey

Big Brain Data: On the Responsible Use of Brain Data from Clinical and Consumer-Directed Neurotechnological Devices

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