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In-Depth Information
TABLE 3.3
Comparison of Data-Auditing Schemes
Public
Verifiability
Privacy
Preserving
Detection
Probability
Scheme
Data Dynamics
PDP
Ateniese et al. [2]
Ye s
Append only
No
1 − (1 −
p
)
c
Ateniese et al. [3]
No
Ye s
No
1 − (1 −
p
)
c
Sebé et al. [35]
No
No
No
1 − (1 −
p
)
c
Erway et al. [10]
No
Ye s
No
1 − (1 −
p
)
c
PoR
Juels and Kaliski [20]
No
No
No
1 − (1 −
p
)
c
Shacham and Waters [36]
No
No
No
1 − (1 −
p
)
c
HAIL: Bowers et al. [7]
No
No
No
1 − (1 −
p
)
c
Wang et al. [40]
Ye s
Ye s
Ye s
1 − (1 −
p
)
cs
Hao et al. [17]
Ye s
Ye s
Ye s
1 − (1 −
p
)
cs
a verification scheme that takes these issues into account. Specifically, to
achieve efficient data dynamics, they improved the present proof of stor-
age models by manipulating the classic Merkle hash tree construction for
block tag authentication. They explored a bilinear aggregate signature to
support efficient handling of multiple auditing tasks and extend output into
a multiuser setting, where TPA can perform multiple auditing tasks simul-
taneously. This theme achieves batch auditing wherever multiple delegated
auditing tasks from totally different users are often performed at the same
time by the TPA.
Hao et al. [17] described a remote data integrity-checking protocol that
supports public verifiability, data dynamics, and privacy against verifiers
without any TPA. They used RSA-based homomorphic verifiable tags for
their protocol construction.
Table 3.3 shows the comparative analysis of different data-auditing
schemes. We indicate if the scheme is probabilistic or deterministic, whether
public verifiability is satisfied, if the scheme can support dynamic data, and
if the scheme is privacy preserving. We compare the detection probability in
each case. Here,
c
is the number of blocks sampled, and
p
is the probability
that a block is corrupted.
3.5 Conclusion and Future Work
In this chapter, we discussed a few security issues in cloud computing.
Most of the techniques help us to protect against dishonest CSPs. Cloud
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