5. EXISTING SYSTEM
Data are produced at a large number of sensor node sources
and processed in-network at intermediate hops on their way
to a base station (BS) that performs decision-making.
Existing system employs separate transmission channels for
data and provenance.
The traditional provenance security solutions use intensively
cryptography and digital signatures and they employ
append-based data structures to store provenance, leading to
prohibitive costs.
6. DISADVANTAGES
It is not intended as a security mechanism. So It does not
deal with malicious attacks.
Provenance in sensor networks has not been properly
addressed.
Sensors often operate in an untrusted environment,
where they may be subject to attacks. Hence, it is
necessary to address security requirements such as
confidentiality, integrity and freshness of provenance.
7. Literature Survey
Secure Provenance Transmission for Streaming Data: ( Aug
2013)
The process of Keeping track of data provenance in such highly dynamic
context is an important requirement, since data provenance is a key factor
in assessing data trustworthiness which is crucial for many applications.
Provenance management for streaming data requires addressing several
challenges, including the assurance of high processing throughput, low
bandwidth consumption, storage efficiency and secure transmission.
Provenance-Aware Storage systems:(June 2012)
A Provenance-Aware Storage System (PASS) is a storage system that
automatically collects and maintains provenance or lineage, the complete
history or ancestry of an item.
The advantages of treating provenance as meta-data collected and
maintained by the storage system, rather than as manual annotations
stored in a separately administered database.
A PASS implementation, discussing the challenges it presents,
performance cost it incurs, and the new functionality it enables. The
reasonable overhead, provide useful functionality not available in today's
8. A Survey of Data Provenance in E-Science:(2011)
Data management is growing in complexity as large scale applications take
advantage of the loosely coupled resources brought together by grid
middleware and by abundant storage capacity.
The main aspect of our taxonomy categorizes provenance systems based
on why they record provenance, what they describe, how they represent and
store proven
Provenance-Based Trustworthiness Assessment in Sensor
Networks:(2011)
A systematic method for assessing the trustworthiness of data items is created . Our
approach uses the data provenance as well as their values in com-putting trust scores,
that is, quantitative measures of trust worthiness.
To obtain trust scores, a cyclic framework which well reflects the Inter
dependency property: the trust score of the data affects the trust score of the network
nodes that created and manipulated the data, and vice-versa.
The trust scores of data items are computed from their value similarity and
provenance similarity.
The value similarity comes from the principle that “the more similar values for the
same event, the higher the trust scores”. The provenance similarity is based on the
principle that “the more different data provenances with similar values, the higher the
trust scores”.
9. Chimera: A Virtual Data System for Representing, Querying,
and Automating Data Derivation:(2010)
A lot of scientific data is not obtained from measurements but
rather derived from other data by the application of computational
procedures.
The hypothesize that explicit representation of these procedures
can enable documentation of data provenance, discovery of
available methods, and on-demand data generation (so-called
"virtual data").
To explore this idea, we have developed the Chimera virtual data
system, which combines a virtual data catalog for representing data
derivation procedures and derived data, with a virtual data language
interpreter that translates user requests into data definition and query
operations on the database.
The Chimera system with distributed "data grid" services to
enable on-demand execution of computation schedules constructed
from database queries.
10. PROPOSED SYSTEM
The proposed technique relies on in-packet Bloom filters to
encode provenance.
The secure provenance scheme with functionality to detect
packet drop attacks staged by malicious data forwarding
nodes.
For transmitting data and provenance we require only single
channel.
Bloom filters make efficient usage of bandwidth, and they
yield low error rates in practice.
The efficient techniques for provenance decoding and
verification at the base station is done .
11. ADVANTAGES
The security mechanism is improved.
Malicious attacks are handled.
Sensor network in provenance is addressed properly.
The detailed security analysis and performance
evaluation of the proposed provenance encoding scheme
and packet loss detection mechanism.
13. References
S. Sultana, M. Shehab, and E. Bertino, “Secure Provenance Transmission
for Streaming Data,” IEEE Trans. Knowledge and Data Eng., vol. 25, no.
8, pp. 1890-1903, Aug. 2013.
S. Roy, M. Conti, S. Setia, and S. Jajodia, “Secure Data Aggregation in
Wireless Sensor Networks,” IEEE Trans. Information Forensics and
Security, vol. 7, no. 3, pp. 1040-1052, June 2012.
S. Sultana, E. Bertino, and M. Shehab, “A Provenance Based Mechanism
to Identify Malicious Packet Dropping Adversaries in Sensor Networks,”
Proc. Int’l Conf. Distributed Computing Systems (ICDCS) Workshops,
pp. 332-338, 2011.
S. Papadopoulos, A. Kiayias, and D. Papadias, “Secure and Efficient In-
Network Processing of Exact Sum Queries,” Proc. Int’l Conf. Data Eng.,
pp. 517-528, 2011.
W. Zhou, M. Sherr, T. Tao, X. Li, B. Loo, and Y. Mao, “Efficient
Querying and Maintenance of Network Provenance at Internet- Scale,”
Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 615-626,
2010.
