=Paper=
{{Paper
|id=Vol-2906/paper2
|storemode=property
|title=Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare
|pdfUrl=https://ceur-ws.org/Vol-2906/paper2.pdf
|volume=Vol-2906
|authors=Syeda Amna Sohail
|dblpUrl=https://dblp.org/rec/conf/caise/Sohail21
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==Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare==
https://ceur-ws.org/Vol-2906/paper2.pdf
Normative and Empirical Evaluation of Privacy
Utility Trade-off in Healthcare
Syeda Amna Sohail[0000−0001−8078−0411]
Department of Data Management and Biometrics (DMB), Faculty of Electrical
Engineering, Mathematics and Computer Science (EEMCS), University of Twente,
Enschede, The Netherlands
s.a.sohail@utwente.nl
http://www.utwente.nl
Abstract. Post-GDPR, the public/private (healthcare) enterprises, while
performing (sensitive) Big Data Analytics (BDA), encounter the dilemma
of abiding by the privacy regulations on one hand and extracting max-
imum value from (healthcare) metadata on the other. Concerning this,
one of the major issues is the Privacy Utility trade-off (PUT). The
PUT affects each phase including (healthcare) metadata collection, for-
mulation, storage, and resharing amongst (healthcare) enterprises. So
far in healthcare, PUT concerning issues are identified and resolved in
a remote, disintegrated manner. It’s high time to resolve the issue by
taking a holistic approach. This Ph.D. research work strives to achieve
the same with normative (should be) and empirical (as-is) evaluation of
PUT in Dutch care metadata share landscape. For clarity, the problem
area is segregated into four fundamental dimensions. For each dimen-
sion, empirical evaluation is performed using Process Mining (discov-
ery/conformance checking) techniques on real-world healthcare event-
log(s). Based on data analytics, the conceptual modeling frameworks
are formulated using e3 value modeling or/and REA ontologies. For
normative evaluation, two alternative approaches; the ‘Content Anal-
ysis’, to formulate the conceptual modeling framework(s) and ‘BPMN
text extraction’, for documents ‘Rule Mining’ for drawing the respec-
tive business model(s), are used. Later, the (in-field) IT expert(s) fur-
ther evaluates the proposed conceptual model(s). The aim is to evaluate
the technical (IS-based privacy-preserving tools and techniques) and re-
spective organizational (access governance, data ownership) measures of
Dutch healthcare providers. The research work will (ultimately) con-
tribute standardized conceptual modeling framework(s) with technical
and respective organizational measures to efficiently cope with the PUT
in handling sensitive (healthcare) metadata.
Keywords: privacy utility tradeoff · conceptual modeling · process min-
ing · healthcare.
Copyright ©2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
12 Syeda Amna Sohail
1 Introduction
Information Technology (IT) is quintessential in how contemporary research and
industrial undertakings proceed and execute [16]. IT (computers, software apps,
and telecommunication) together with the business process modeling (and evalu-
ation) created Information Technology Engineering (ITE) [10]. ITE is an amalga-
mation of business processes, techniques, and systems that improve business pro-
ceedings in better achieving business goals. Because of ITE, public/private enter-
prises formulate, store and share valuable metadata (i.e. data/information about
other (big) data) for Big Data Analytics (BDA). Big Data Analytics (BDA) is
(meta) data evaluation for valuable information gain. The BDA of metadata fa-
cilitates the extraction of insights and actionable decisions in achieving business
goals in a cost and time-efficient manner [11]. In healthcare, the BDA improves
the care business models, foresees the long/short term treatment outcomes, and
does patient and disease centric stratification [11]. Post Covid-19, the BDA is
essential in aiding infectious control measures and respective policies. However,
the use of BDA predominantly relies upon metadata sharing and posits some
serious ethical concerns including privacy preservation of patient’s personally
identifiable information [5, 6, 8] within and across healthcare sub-domains.
Privacy comprises the autonomous decision-making and direct/indirect con-
trol over personal information [13]. The privacy concerns do not imply the lack
of trust in BDA, rather it demands responsible and fair metadata sharing [4, 6].
Unfortunately, the pace of privacy-preserving tools’ (and techniques) formulation
(and implementation) lag far behind in comparison to the use of BDA across do-
mains especially in healthcare [8, 13]. Caregivers, for patients’ effective/efficient
clinical care, are bound to share the un-anonymized/pseudonymized patients’
metadata with other internally and externally located counterparts such as labs
and pharmacies [23]. Simultaneously, the care providers are obliged to fulfill
the privacy legislature/regulations [2, 3] to avoid paying hundreds of thousands
of Euros as compensation [23]. For example, Haga hospital and Menzis insur-
ance company had to compensate for privacy lapses with hefty amounts [23].
Privacy-Utility-Tradeoff (PUT) is the performance impairment of data an-
alytics in ascertaining data privacy [21]. The issue demands quick, comprehen-
sive, and efficient technical/organizational solutions, to identify and evaluate
the data utility-prone privacy-preserving measures for the care provider’s Infor-
mation Systems (ISs). Such measures will facilitate the healthcare providers to
avoid paying hefty compensations and in turn infamous reputation.
Section 1 (introduction) comprises introduction of the problem domain and
two subsections namely the related work and research objectives and research
questions where we highlight the current state of the art and this research work’s
objectives and questions, Section 2 comprises the standardized data analytics
approach and research methodology for all four dimensions. In Section 3, we
present the current results employing the two currently published papers
and an under-review paper in the first year of Ph.D. research work. Section
4 highlights the threats to the validity of this research work, Section 5 gives a de-
tailed description of the dimension-wise contribution and the uniqueness of this
Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare 13
research work. Section 6 includes a conclusion, acknowledgments and is followed
by references.
1.1 Related Work
In the contemporary world of the information economy, the BDA is essential
for (private/public) enterprises to shape their business goals and information
system engineering with privacy by design measures [6, 25]. To ensure the re-
sponsible Business Information System Engineering (BISE) the main concern,
amongst others, is the Privacy Utility Tradeoff (PUT) [6, 22]. In healthcare, the
PUT raises graver repercussions because of the involvement of (highly) sensi-
tive personally identifiable data on one hand and the efficiency and effectiveness
of healthcare performance on the other [23]. In this regard, research studies (in
healthcare) remotely focus on privacy and IoT [12,16], privacy in AI and machine
learning [13,19], privacy in Process Mining (focussing on third party process an-
alytics) [17, 21, 27] by not paying much attention to the overall context of the
issue. Similarly, the data pipeline (from data collection to valuable insights) and
provenance records (pipeline description) is often ignored while sharing of the
data is emphasized concerning PUT [20].
It is important here to realize that PUT concerning issues of (healthcare)
BDA is both technical and organization-based. Thus, the issue requires an eval-
uation of (care provider’s) integrated techniques, processes, and systems (i.e.
ITE) in (care) metadata share landscape. Privacy by design only provides for
the technical solutions of real-world problems [25] but the people performing the
tasks behind their computer screens are the quintessential source of issue resolv-
ing. People who are interwoven in the organization’s architectural setup and are
accountable to the organization. Besides, to identify, ’what (i.e. techniques) is
happening in the BISE’, it is integral to identify and evaluate that how (i.e. busi-
ness processes) it is happening in that fashion? Process Mining with the event
logs gives us a glimpse of the same using the datasets extracted directly from an
organization’s IS. Moreover, to check the business system’s and processes’ com-
pliance on both technical and organizational grounds, the normative (should be)
evaluation is done. Normative evaluation is done using two alternative method-
ologies namely, ’Content Analysis’ and organization’s (official) documents’ rule
mining using ’BPMN text extraction’. For simplification, the findings are repre-
sented with conceptual modeling frameworks using REA and e3 value modeling
ontologies. The ontologies are further evaluated by (in-field: working in the same
domain) IT experts.
1.2 Research Objectives and Research Questions
The method of the research work aims to answer dichotomous knowledge ques-
tions (Design Science Methodology [28]), that include Analytical research work,
using Process Mining tools/algorithms on real-world healthcare event log(s) (i.e.
data set(s) that are extracted directly from healthcare provider’s Information
Systems) for the empirical (as-is state of affairs) evaluation of PUT in Dutch care
14 Syeda Amna Sohail
FAIR/FACT
based data?
evaluation
Data Utility Data Quality
indicators and indicators and Data Ecosystem
quantification quantification Integration etc
evaluation evaluation measures i.e. from
2. Quantification logistics domain
of Data Value
1. Privacy 4. Co-operate to Compete:
Access other domains' suitable
Preservation (inter and
governance Privacy Utility Tradeoff privacy preserving, utility-
intra organizational) in
evaluation (PUT) in Healthcare prone tools and techniques
care metadata sharing)
Technical Solutions
3. Data Privacy Indicators privacy preserving, utility prone tools and
Ownership and quantification methods techniques which are pro
evaluation evaluation legislative/regulatory prerequisites
Fig. 1: Four fundamental dimensions to evaluate the Privacy Utility Trade-off
(PUT) in (care) metadata share landscape.
metadata share landscape. And Exploratory research work, using either ’Con-
tent Analysis’ or the ’BPMN text extraction’ for normative (should-be state of
affairs) evaluation. The goal is the normative and empirical evaluation of PUT
concerning tools/techniques in the care provider’s technical and organizational
metadata sharing set-up.
For clarity the research goal is subdivided into Four dimensions (see Fig.1)
based on four sub-research objectives: Objective 1 : Evaluate privacy mea-
sures in care metadata share landscape within Dutch care providers’ inter/intra
organizational setup. Objective 2 : Identify the data utility and data quality indi-
cators in healthcare and assess whether they are pro/against privacy indicators
(PUT) on one hand and FAIR and FACT-based data indicators on the other.
Objective 3 : Evaluate both normatively and empirically the data ownership and
access governance on both technical and organizational grounds in the care meta-
data share landscape. Objective 4 : identify privacy-preserving, higher data utility
prone measures from other domains (i.e. logistics, etc) that are effectively ap-
plicable to healthcare and are per regulatory and legislative requirements of the
EU.
The respective four dimensions (each with an assigned color see
Fig.1) and their Research Questions (RQs) are as follow: Dimension 1 :
Privacy evaluation in care metadata share landscape at the backdrop of care
providers inter and intra-organizational setup. RQ1 : What are the privacy-
preserving indicators in the care metadata share the landscape, how are they
implemented and assessed in Dutch inter and intra-organizational setup? Di-
mension 2 : Quantification of the data value in healthcare and its relevance to
privacy (PUT) and FAIR and FACT-based data. RQ2.1 : What are the respec-
tive indicators for data utility and data quality while sharing healthcare meta-
data in inter/intra organizational setup. RQ2.2 : How respective indicators sup-
port/ discourage the privacy indicators on one hand and FAIR (uninterrupted
data) and FACT (responsible data) based data indicators on the other? Dimen-
Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare 15
sion 3 : Privacy evaluation of data ownership in healthcare metadata within and
amongst care providers. RQ3 : What is the normative (should be) and empirical
(as is) state of affairs of data ownership and access governance in care meta-
data share at inter/intra organizational levels? Dimension 4 : Identify privacy-
preserving, higher data utility prone measures from other domains i.e. logistics,
etc, that are effectively applicable to healthcare and are per regulatory and
legislative requirements of the EU. RQ4.1 : What are useful privacy-preserving
tools/techniques in safeguarding an organization’s integrity in addition to the si-
multaneous sharing of valued metadata with other counterparts for the collective
benefit? RQ4.2 : How are those privacy-preserving, data utility-prone measures
applicable to healthcare, and are they effective? Give normative and empirical
evaluation.
2 Data Analytics Approach and Research Methodology
For each dimension (see Fig. 1), the following standardized data analytics ap-
proach (and methodology) is applied (see Fig. 2 for an overview). The approach
combines the empirical and normative evaluation and aims to validate care
providers’ integrated techniques, processes, and systems concerning PUT in the
Dutch (care) metadata share landscape. Empirical evaluation is done using Pro-
cess Mining (PM) discovery and conformance checking techniques (to validate
the empirical evaluation) on healthcare event logs i.e. datasets that are extracted
directly from Hospital Information System (HIS) [26]. The objective is to eval-
uate data utility in comparison to privacy preservation of sensitive data. Based
on the empirical analytics, conceptual modeling frameworks are drawn (using
REA or e3 value modeling ontologies) and are evaluated by in-field (i.e. from
within the organization) IT expert(s). The aforementioned conceptual model-
ing frameworks are selected for clarity and convenient understanding of the key
actors, their interactions, and mutual value gain for technical (and organiza-
tional) proceedings. [23]. For normative evaluation, two alternative approaches
are followed as per the respective research objective to ascertain the provenance
record (briefly explained in the next paragraph) making. One approach follows
the ‘Content Analysis’ (using literature review, official websites, and (online)
content) methodology to formulate conceptual modeling framework(s). We also
plan to include the ‘BPMN text extraction’ methodology for document rule min-
ing [1]. The deduced BPMN model will be evaluated by the IT expert(s). So far,
publicly available event logs (comprising metadata share within and across a
local hospital) are used for research findings, we are in process of a prospective
collaboration with an EU project for some interesting datasets and respective
project collaboration concerning inter-organization exchange of health data with
a special focus on privacy preservation.
The PUT concerning issues either depend upon or directly influence the
prescribed first three dimensions. Which in turn incorporate the data pipeline
and data provenance [18]. Data pipeline allows automatic data gathering from
diverse sources and its integration into a data warehouse. In healthcare, espe-
16 Syeda Amna Sohail
Data acquisition/integration Data extraction from Hospital Data analytics using Deduced business models Further data evaluation by
from multiple sources Information System (HIS) Process Mining (PM) (PetriNets) evaluation in-field IT experts
Multiple Data sources
(As-is state of affairs)
Empirical evaluation
Data Analytics Petri Nets Expert Opinion
HIS Event log (Process Mining) Evaluation (in-field)
Publicly/privately
available real-world
(care) datasets
Data Analytics
Conceptual Modeling
Literature Review, Websites (Content Analysis)
(REA,e3 value modeling) AND
(Shoud-be state of affairs)
Normative evaluation
Data Analytics Business Model
Documents (Rule Mining) (BPMN Text Extraction)
Data acquisition from literature Content Analysis to locate standardized Conceptual modeling
review, official websites, practice principals for privacy by design/ framework using
online content privacy by policy for care data REA, e3 value-modeling Further evaluation by
XOR XOR XOR in-field IT experts
Documents Rule Mining to locate
Data acquisition from care standardized practice principals for privacy Business model using
providers' official documents by design/privacy by policy for care data BPMN Text Extraction
Fig. 2: Data Analytics Approach (with Methodology)
cially in HIS, the data is assimilated from diverse sources that include, amongst
others, administrative, medical, and physiological (including sensor-based) data
resources. Provenance is the documentation of the “data entities, systems and
processes” to avoid data manipulation and system misuse [18]. The potential
usefulness of provenance records depends directly upon the veracity (genuine
description) of the data pipeline. Provenance records allow: easier assessment
in complying with the regulatory prerequisites, identification, and recovery of
bottlenecks concerning systems and processes, and data security and privacy
preservation [18]. The aforementioned methods and their combination were in-
tentionally selected for not only analytical but also normative evaluation of PUT
to validate the integrity of the systems, tools, and processes in the contemporary
Dutch healthcare landscape. The prescribed fourth dimension, however, largely
relies upon the availability of the time.
3 Current Results
By following the above-mentioned approach and methodology, two papers are
published and one paper is under review [23, 24]. Normative evaluation is done
using the Content Analysis (explained above) methodology [24]. A conceptual
modeling framework is designed on the fundamentals of the Padlock Chain Model
of e3 value modeling. The model is evaluated by the (in-field) IT expert (An IT
head in a local hospital who is also affiliated with a local diagnostic lab). The
model identified that the privacy is implemented with ’privacy by design, ’pri-
vacy by policy, and patients ’informed consent for care metadata sharing. The
indicators for privacy by design vary as per each healthcare provider’s business
goals, and lack consistency across healthcare providers [24]. Privacy by pol-
Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare 17
icy (Information Security Management System) indicators are regulated by ISO
(and NEN) and local regulatory authorities but these are only qualitative eval-
uations. The lack of; consistent/above board privacy by design indicators and
quantitative evaluation of ISMS indicators, leaves ample room for technical and
organizational ambiguity for the care providers and in turn, gives vent to the
privacy lapses [24].
Another research work identifies that the un-anonymized/pseudonymized
care metadata sharing within/across a local hospital poses serious (patients’)
privacy concerns [23]. The empirical evaluation was done using Process Mining
on event logs from a local HIS. The evaluation identified that for the sake of effi-
cient/effective performance-oriented care, the patients’ un-anonymized metadata
is shared within horizontally oriented intra-organizational (i.e. between multiple
departments within the hospital, etc) caregivers [23]. The in-field IT expert eval-
uated that the findings are generalizable to the vertically located (i.e. outpatient
caregivers such as lab, general practitioner, pharmacy, hospital) caregivers as
well. Based on the aforementioned empirical evaluation and (normative) content
analysis, the conceptual modeling framework using REA’s Insurance Model [14]
is extended [23]. The framework stresses recent advancements where ’Material-
ized Privacy Claims’ are launched either by the patient or by any other potent
authority, such as the Dutch Data Protection Officer (DPO) and costs hundreds
of thousands of euros to the care providers [23].
Another under review research work evaluates the PUT on care event log
from HIS. The PUT is evaluated using the ProM tool for identifying noise-
adding plugins which are data utility efficient as well. The plugins are evaluated
on three different datasets and two different versions of ProM and gave similar
results. The research work will assist the ProM tool’s end-users to make use of
those plugins for privacy-preserving, utility-prone data analytics using Process
Mining. So far, the first dimension and marginally the second dimension are
covered. The rest of the dimensions will be covered in the remaining years to
come.
4 Threats to Validity
To increase the scope and to ascertain the functionality of the data analyti-
cal approach, we are not confining ourselves to one disease-specific event log(s),
rather an analysis of more diversified datasets (with a focus on inter/intra or-
ganizational data exchange) will allow us to conduct more realistic empirical
evaluation (in avoiding the selection bias). But on the other hand, this approach
can lead us to certain unforeseen pitfalls while aggregating the data information
as the results will be diversified yet non-conforming to one another. To avoid this
loophole, in the future, we aim to gather at least two or more event logs from
a similar sub-domain. Additionally, to gather the diversified datasets, we are
in discussion with the personnel who are directly involved with an EU project
and are interesting in a prospective collaboration with us concerning inter/intra
organizational care data exchange with a special focus on privacy preservation.
18 Syeda Amna Sohail
1. Clarity regarding privacy definition and
approach for the normative and empirical
Dimension wise contribution of a holistic
its qualitative assessment measures. Policymakers need to
standardize the Privacy by Design measures and entrepreneurs
evaluation of PUT in healthcare
have to apply standardized technical measures to effectively
safeguard privacy to avoid hefty compensations.
2. Understanding that the FAIR data
4. Evalua on supports utility prone metadata sharing, whereas FACT
of privacy-preserving, data u lity prone tools, CONTRIBUTION measures facilitate privacy-preserving ethical data sharing.
and techniques from other domains and their (DIMENSION-WISE) Resolving PUT issues in care metadata sharing will extend
evalua on in healthcare the implications to FAIR and
FACT-based data.
3. Understanding the target areas
regarding data ownership and access governance both
technically and organization-wise. Giving
recommendations for both
Fig. 3: Dimension-wise contribution to the PUT evaluation
So far the discursive technical solutions hamper the holistic understanding of
the PUT in the care data share landscape. This research work aims to provide
the footing for the same. Even if all four dimensions are not completed by the
end of this Ph.D. work, at least it will provide the infrastructure with the first
three dimensions which will (ideally) serve the basic purpose/goal.
5 Contribution
So far, various divergent, remotely conducted analytical research works in health-
care such as for Internet of Medical Things (IoMT) [12], Artificial Intelligence
(AI) in healthcare [8, 13], generic IT-related challenges [16], Process Mining [21]
exist. Similarly, normative evaluation with the standardized privacy-aware con-
ceptual modeling frameworks for the scalable, privacy-preserving systems for
IoT exist [7, 9, 15]. The connection between the empirical and normative evalu-
ations of PUT in care metadata sharing is still lacking. The proposed research
work proposes a unique holistic approach in resolving PUT concerning issues
in healthcare. Fig. 3 (with a recurring color scheme for each dimension as that
of Fig. 1) gives a detailed description of the dimension-wise contribution of the
proposed research work (see Fig. 3).
6 Conclusion
ITE is an amalgamation of business processes, techniques, and systems that im-
prove business proceedings in better achieving business goals. Currently, enter-
prises encounter privacy concerning issues in performing performance-oriented
data analytics. Privacy-Utility-Tradeoff (PUT) is the performance impairment
of Big Data Analytics (BDA) in ascertaining data privacy. Normative (should
be) and empirical (as-is) evaluation of PUT is essential to better find suitable
techniques and processes for (care providers) Information Systems against PUT
in healthcare.
The empirical evaluation is performed on real-world care event logs. The find-
ings are drawn using conceptual modeling frameworks using REA and e3 value
Normative and Empirical Evaluation of Privacy Utility Trade-off in Healthcare 19
modeling ontologies. For normative evaluation, two alternatives approaches are
followed. One content analysis technique provides a basis for the ’conceptual
modeling’ frameworks and the other ’BPMN text extraction’ allows documents
rule mining and formulation of BPM. (In-field) IT experts further evaluated the
conceptual models.
By following the afore-mentioned data analytics approach and methodology,
two papers are published and one paper is under review in the first year of
this Ph.D. research work. The first paper identified the loopholes in privacy
by design and privacy by policy measures. The second paper identified the un-
anonymized/pseudonymized care metadata share amongst horizontally and ver-
tically located caregivers in the Dutch metadata share landscape. Another under-
review paper locates the privacy-preserving data utility-prone noise-adding plu-
gins in publicly available PM tool i.e. ProM.
The future work comprises the quantification of the data value in healthcare
and its relevance to privacy (PUT) and FAIR and FACT-based data, privacy
evaluation of data ownership/stewardship in healthcare metadata within and
amongst care providers, identification of privacy-preserving, higher data utility
prone measures from other domains (i.e. logistics, etc) and their evaluation in
healthcare. PUT concerning issues of (healthcare) BDA is both technical and
organization-based. Thus, the issue requires evaluation of (care provider’s) inte-
grated techniques, processes, and systems to find respective solutions in (care)
metadata share landscape.
Acknowledgments. I am grateful to my mentor, dr. Maurice van Keulen, and
my first supervisor, dr. Faiza Allah Bukhsh for their exceptionally discerning
reviews, their undaunting support, and guidance, throughout.
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