ABSTRACT
In
the problem of privacy-preserving collaborative data publishing (PPCDP), a
central data publisher is responsible for aggregating sensitive data from
multiple parties and then anonymizing it before publishing for data mining. In
such scenarios, the data users may have a strong demand to measure the utility
of the published data since most anonymization techniques have side effects on
data utility. Nevertheless, this task is non-trivial because the utility
measuring usually requires the aggregated raw data, which is not revealed to
the data users due to privacy concerns. What’s worse, the data publishers may
even cheat in the raw data since no one including the individual providers
knows the full dataset.
In
this paper, we first propose a privacy-preserving utility verification mechanism
based upon cryptographic technique for DiffPart – a differentially private
scheme designed for set-valued data. This proposal can measure the data utility
based upon the encrypted frequencies of the aggregated raw data instead of the
plain values, which thus prevents privacy breach. Moreover, it is enabled to
privately check the correctness of the encrypted frequencies provided by the
publisher, which helps detect dishonest publishers. We also extend this
mechanism to DiffGen – another differentially private publishing scheme
designed for relational data. Our theoretical and experimental evaluations demonstrate
the security and efficiency of the proposed mechanism.
Existing Definition
Ø A
lot of privacy models and corresponding anonymization mechanisms have been
proposed in the literature such as k-anonymity and differential privacy.
Ø k-anonymity
and its variants (e.g. l-diversity and t-closeness protect
privacy by generalizing the records such that they cannot be distinguished from
some other records. Differential privacy is a much more rigorous privacy model.
It requires that the released data is insensitive to the addition or removal of
a single record.
Proposed
Solution:
v We
first propose a privacy-preserving utility verification mechanism for DiffPart,
a differentially private anonymization algorithm designed for set-valued data.
v DiffPart
perturbs
the frequencies of the records based on a context-free taxonomy tree and no
items in the original data are generalized.
v Our
proposal solves the challenge to verify the utility of the published data based
on the encrypted frequencies of the original data records instead of their
plain values. As a result, it can protect the original data from the verifying
parties (i.e., the data users) because they cannot learn whether or how many
times a specific record appears in the raw dataset without knowing its real
frequency. In addition, since the encrypted frequencies are provided by the
publisher, we also present a scheme for the verifying parties to incrementally
verify its correctness.
v We
then extend the above mechanism to DiffGen, a differentially private
anonymization algorithm designed for relational data. Different from DiffPart,
DiffGen may generalize the attribute values before perturbing the
frequency of each record. Information losses are caused by both the
generalization and the perturbation. These two kinds of information losses are
measured separately by distinct utility metrics.We take both into
consideration.
v Our
analysis shows that the utility verification for generalization operations can
be carried out with only the published data. As a result, this verification
does not need any protection. The utility metric for the perturbation is
similar with that for DiffPart.We thus adapt the proposed
privacy-preserving mechanism to this verification.
We conduct a series of experiments
upon the real world set-valued data and relational data to evaluate the
efficiency of the proposed mechanisms. The results show that these mechanisms
are efficient enough provided that both the data publishing and utility
verification are offline.
System
Modules:
Ø
USER
Ø Register
Ø Login
Ø Generate
OTP
Ø Ftp
Cloud
Ø File
upload/ Download
MODULES:
Ø USER:
Ø Register:
User enters this system and register
with own details.
Ø Login:
User can login this system after they
can view home page.
Ø Generate OTP
User can login this system before
they are getting OTP for continue login
through e-mail.
Ø FTP Cloud:
User can login this system after they
can view home page of cloud system.
Ø File Upload/Download
User can enter this system after they
can File upload/ download to this system.
Use case Diagrams:
Sequence Diagram:
project-center-trichy-thanjavur-kumbakonam
project-center-salem-erode-namakal-tiruchengode-karur-gandhipuram
project-center-mannargudi-pattukkottai
project-center-ambattur-avadi-ashokpillar-adyar-ekkaduthangal
project-center-bangalore-chennai-trivandrum
project-center-bhubaneswar-belgum-bhopal
project-center-chidambaram-mayiladuthurai-nagapattinam-cuddalore
project-center-coimbatore-chennai-salem-madurai-erode-trichy-tirunelveli-pondicherry
project-center-delhi-mumbai-hyderabad-visakhapatnam
project-center-dharmapuri-hosur-krishnagiri
project-center-dindigul-palani-rasipuram
project-center-tirunelveli-tiruchendur-nagercoil-virudhunagar-rajapalayam
project-center-tnagar-tambaram-nungambakkam-velachery
project-center-trivandrum-ernakulam
project-center-in-chennai
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