Redacting DATA Provenance TEAM AVIni SOGANI VAISHNAVI SUNKU VENUGOPAL BOPPA Internet of things Semantic web and provenance Meaning behind anything you say Semantic web is the platform that provides secure sharing of heterogeneous data on the web ID: 694979
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Slide1
Quantifying INFORMATION LOSS after Redacting DATA Provenance
TEAM:
AVIni SOGANI
VAISHNAVI SUNKU
VENUGOPAL BOPPASlide2
Internet of thingsSlide3
Semantic web and provenance
Meaning behind anything you
say
Semantic
web is the platform that provides secure sharing of heterogeneous data on the web.
Provenance of data can be traced down to the origin of the data or can be simply an immediate source.
Provides assessment of authenticity, enables trust, and provides assurance for data quality and thereby allows reproducibility of that resource. Slide4
REDACTION
Imposing restrictions to data access by
users
T
ypes – DAC, MAC, RBAC
Process of removing or hiding sensitive data
Protect sensitive information from unauthorized usersSlide5
Related workSlide6
Privacy control acts
HIPAA – Health Insurance Portability and Accountability Act
Regulates EMR/EPR
PHI – Protected Health Information
PII – Personally Identifiable Information
HITECH Act – Health Information Technology for Economic and Clinical Health
Minimum necessary for the stated purposeSlide7
W3C Recommendations
A.C. model applications
File systems
Database
Provenance
?
Data
Models:
RDF (Triples, subject, predicate, object)OPM
Querying:
OPQL (From(e), to, from
-1
(n), to
-1
,
prev
(n), next)
SPA
RQL
(Regular expressions)Slide8
Redaction policies
Medical ScenarioSlide9
Redaction on data provenanceWhy med: Doc1_2?Slide10
Redaction by graph grammar and R.e.Slide11
Architecture Slide12
Limitations Slide13
No Quantification of the information lost by the process of redaction
The availability of redacted information available from different source (internet, knowledge of the context..)Slide14
Our proposalsSlide15
Information Loss
Relevance of the data to the user
Vectorial
model formula for calculating the relevance
Terms:
True relevant data
Retrieved data
Relevant data
F Measure (precision and recall)
NMI (Normalized Mutual Information)Slide16
Information lossSlide17Slide18
conclusionSlide19
References:
Query Language Constructs for
Provenance,
Murali
Mani, Mohamad Alawa,
Arunlal
Kalyanasundaram
T
yrone
Cadenhead
,
Vaibhav
Khadilkar
, Murat
Kantarcioglu
, and
Bhavani
Thuraisingham
. 2011. Transforming provenance using redaction. In Proceedings of the 16th ACM symposium on Access control models and technologies (SACMAT '11). ACM, New York, NY, USA, 93-102
.
Tyrone
Cadenhead
,
Vaibhav
Khadilkar, Murat Kantarcioglu and Bhavani
Thuraisingham, A Language for Provenance Access ControlNettleton, David F., and Daniel Abril. "An Information Retrieval Approach to Document Sanitization." Advanced Research in Data Privacy. Springer International Publishing, 2015. 151-166.
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search, 2nd
edn. ACM Press Books, England (2011)Slide20
Thank you..