Obscuring Provenance Confidential Information via Graph Transformation

Hussein, Jamal Ali; Moreau, Luc; Sassone, Vladimiro

doi:10.1007/978-3-319-18491-3_8

Cited by 7 publications

(5 citation statements)

References 22 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mohy et al [41] propose a workflow provenance sanitization approach, called ProvS, which combines both anonymization and grouping. Hussein et al [35] propose PROV-GTS, a provenance graph transformation system that avoids false dependencies and independencies. As opposed to these works, which attempt to generate provenance graphs that preserve the privacy of the provenance aspects that are to be hidden while seeking to provide high utility, we focus on the verification of the correctness of the workflow output in statistical studies, which, to the best of our knowledge, has not been studied before.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Halimi¹,

Dervishi²,

Ayday³

et al. 2021

Preprint

View full text Add to dashboard Cite

Providing provenance in scientific workflows is essential for reproducibility and auditability purposes. Workflow systems model and record provenance describing the steps performed to obtain the final results of a computation. In this work, we propose a framework that verifies the correctness of the statistical test results that are conducted by a researcher while protecting individuals' privacy in the researcher's dataset. The researcher publishes the workflow of the conducted study, its output, and associated metadata. They keep the research dataset private while providing, as part of the metadata, a partial noisy dataset (that achieves local differential privacy). To check the correctness of the workflow output, a verifier makes use of the workflow, its metadata, and results of another statistical study (using publicly available datasets) to distinguish between correct statistics and incorrect ones. We use case the proposed framework in the genome-wide association studies (GWAS), in which the goal is to identify highly associated point mutations (variants) with a given phenotype. For evaluation, we use real genomic data and show that the correctness of the workflow output (i.e., that the output is computed correctly by the researcher) can be verified with high accuracy even when the aggregate statistics of a small number of variants are provided. We also quantify the privacy leakage due to the provided workflow and its associated metadata in the GWAS use-case and show that the additional privacy risk due to the provided metadata does not increase the existing privacy risk due to sharing of the research results. Thus, our results show that the workflow output (i.e., research results) can be verified with high confidence in a privacy-preserving way. We believe that this work will be a valuable step towards providing provenance in a privacy-preserving way while providing guarantees to the users about the correctness of the results.

show abstract

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Halimi¹,

Dervishi²,

Ayday³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The primary aim of the W3C standardized provenance is to enable the extensive publication and exchange of provenance over the web [18]. The provenance data model is subject to a set of constraints and inference rules [19], which are useful in validating the provenance information [20] and are essential for preserving graphs integrity when converting provenance graphs [21], [22]. To establish trust of data, these properties must be maintained when capturing provenance information and constructing provenance graphs [23].…”

Section: Conceptual View Of Data Provenancementioning

confidence: 99%

An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

Ahmed

Hussien

2019

UHD J SCI TECH

View full text Add to dashboard Cite

The future of healthcare may look completely different from the current clinic-center services. Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.

show abstract

“…Concerning the last point, a provenance graph with a "hole" (omission), due to access control, might appear to defeat the use of provenance as an audit tool. A solution often proposed in the literature is the abstraction of a provenance graph [44,30] that both hides sensitive information and conserves the semantic information necessary for provenance analyses.…”

Section: Confidentiality: Controlling Access To Provenance Datamentioning

confidence: 99%

Data provenance to audit compliance with privacy policy in the Internet of Things

Pasquier

Singh

Powles

et al. 2017

Pers Ubiquit Comput

View full text Add to dashboard Cite

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.

show abstract

Obscuring Provenance Confidential Information via Graph Transformation

Cited by 7 publications

References 22 publications

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

Privacy-Preserving and Efficient Verification of the Outcome in Genome-Wide Association Studies

An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

Data provenance to audit compliance with privacy policy in the Internet of Things

Contact Info

Product

Resources

About