A.Hari Krishna REGNO:RA1411003010716
G.G.Jaya surya REGNO:RA1411003010746
Asst. Professor (O.G),
Department of CSE,
´k-NN Query : k-NN Query defines “k-Nearest Neighbor
Query”, Nearest neighbors query is a fundamental primitive in spatial
´The time efficiency of querying of the database is
increased by using voronoi algorithm instead of using Euclidian distance
´Multiple keys are used to get the data in multiple steps
which increases the security and confidentiality.
´A secure k-NN query on cloud data is created with multiple
keys and each level of person are given different type of keys respectively,
´The data is uploaded by the data owner which is split
into multiple parts and each part is given a sertain key.
´cloud computing has become an increasingly popular
service for its flexibility and scalability.
´In the Existing cloud database systems the
confidentiality and privacy is compromised.
Improve the security, to create a more time efficient and to reduce space complexity
a new system is proposed.
´Multiple key decryption approach is used to solve the
problem for confidetialty and k-NN query is the solution to get range.
´3 types of keys – Public key(to get the basic
information), Private key(to get the basic info and some more info),Strong
Private key(to get the complete info) are used for the multiple key model
scalar-product-preserving encryption (ASPE) to preserve scalar product
between the query vector and any vector for distance comparison
computation on encrypted databases
W. K. Wong, D.
find k-NN with
because it is prone to chosen-plaintext attacks
diagram algorithm. Instead of returning exact nearest neighbor, they allow a
cloud server to return a relevant data partition.
Engineering (ICDE), 2013
data partition with key sharing
because it is prone to chosen-plaintext attacks
triangulation and order-preserving encryption to solve the secure k-NN
query processing in untrusted cloud environments
S. Choi, 2014.
it can provide
incurs expensive overhead of computation and communication on the users
servers P1 and P2,encrypted data is known only to P1,secret key is just
revealed to P2. P1 collaborate with P2 for the final result.
An architecture for secure cloud computing
find k-NN with
access the private key of data owner
This work used
a symmetric scheme with a secret matrix transformation as a key, Query users interact
with the data owner to process the query without revealing the query. This
means that the data owner need to remain online for all the users.
Secure and controllable
k-nn query over encrypted cloud data with key confidentiality.
Y. Zhu, Z.
Huang, and T. Takagi, 2016.
with key confidentiality
gives information about data owner’s key
´K-NN querying using
Euclidian distance have time complexity of O(n) which is replaced
by voronoi algorithm which have
complexity of O(log(n)).
´Based on the Distributed Two Trapdoors public-key
cryptosystem (DT-PKC), a set of protocols of secure two-party computation is
constructed that will be used as sub-routines of proposed scheme.
keys and multiple key decryption is generated by the DT-PKC.
´Data owner upload the data after encryption to cloud.
´Each query user holds his key and the data owner can
encrypt and decrypt data.
´Supports the data owner offline. Proposed scheme is
secure under the standard semi-honest model. Multiple keys provides security to
´Data Owner – One who can update the database with the
required files in cloud server.
´Data User – One who can have access the data with
three different types of keys.
´KGC Center – To generate the keys with the help of
Cloud Server using random key generation.
´Upload/encrypt-the data is encrypted and uploaded into
´Cloud server-Place where the data is stored and
querying takes place here.
´3 types of keys –
´Public key(to get the basic information)
´Private key(to get the basic info and some more info)
´ Strong Private key(to get the complete info)
entire data is stored in the cloud.A cloud server records all intermediate and final results and cloud
server is able to perform certain computations.
´A cloud service provider provides online computation
services in the system. So the
cloud service provider can offload the calculation task to cloud platform and
collaborates with it to find the k-NN for query user in a privacy-preserving
and Data Owner
´Data are generated by the Data owner, encrypted using
his public key and then outsourced to cloud platform for storage.
´That data is queried by the Data user to get the information
depending on his level and with the help of keys respectively.
´Each Query user holds some private m-dimensional query
points. For the query point q = (q1, q2, … qm), Query user would like to
retrieve the top k records that are closest to the query point according to the
voronoi algorithm. Query user initially sends his query q (in encrypted form)
to Cloud Platform. After this, Cloud Platform and Cloud service Provider
involve in a set of sub-protocols to compute the voronoi distance then retrieve
the k-NN and return encrypted result to the QU. Only the corresponding query
user can decrypt the result points.
Secure K-NN retrieve
´Two secure protocols Secure Minimum and Secure Minimum
Index of n numbers ,based here to build the secure k-NN retrieve scheme. The
goal of Secure Minimum is for CP and CSP jointly compute the encryption and the
encrypted index. Any cipher text can be decrypted using decryption algorithm with
strong private key.
a user interface for the data owner and to create a single interface for
various types of data users.
voronoi diagram algorithm for the querying instead of euclidian distance
a cloud server which is used for querying group of top K-NN information by
using the voronoi algorithm.
multiple keys and decryption of multiple portions of data by the respected
´The problem of supporting k-NN query over encrypted
cloud data is handled, while the data owner cannot share his key with query
users. For this a new solution is proposed with multiple keys to solve the key sharing
problems thoroughly. Proposed scheme can
protect the data confidentiality and query privacy and will get the k-NN data
´ 1 B. Yao, F. Li, and X. Xiao, “Secure nearest
neighbor revisited,” in Data Engineering (ICDE), 2013 IEEE 29th International
Conference on. IEEE, 2013, pp. 733–744..
´ 2E. Kabir, A. Mahmood, H. Wang, and A. Mustafa,
“Microaggregation sorting framework for k-anonymity statistical disclosure
control in cloud computing,” IEEE Transactions on Cloud Computing, vol. PP, no.
99, pp. 1–1, 2015.
´ 3 H. Cui, X. Yuan, and C. Wang, “Harnessing
encrypted data in cloud for secure and efficient image sharing from mobile
devices,” in 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE,
2015, pp. 2659–2667.
´ 4 M. Li, S. Yu, W. Lou, and Y. T. Hou, “Toward
privacy-assured cloud data services with flexible search functionalities,” in
2012 32nd International Conference on Distributed Computing Systems Workshops.
IEEE, 2012, pp. 466–470
´ 5 N. Cao, Z. Yang, C. Wang, K. Ren, and W. Lou,
“Privacypreserving query over encrypted graph-structured data in cloud
computing,” in Distributed Computing Systems (ICDCS), 2011 31st
International Conference on. IEEE, 2011, pp. 393–402.
´ 6 S. Yu, Y. Tian, S. Guo, and D. O. Wu, “Can we beat
ddos attacks in clouds?” IEEE Transactions on Parallel and Distributed Systems,
vol. 25, no. 9, pp. 2245–2254, Sept 2014