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Thursday, 22 February 2018

Efficient and Privacy Aware Data Aggregation in Mobile Sensing(2014)


Efficient and Privacy Aware Data Aggregation in Mobile

 Sensing(2014)

ABSTRACT:
The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high-computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate, which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggregation protocol to obtain the Min aggregate of time-series data. To deal with dynamic joins and leaves of mobile users, we propose a scheme that utilizes the redundancy in security to reduce the communication cost for each join and leave. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead.
EXISTING SYSTEM:
Ø Sensor data aggregation assumes a trusted aggregator, and hence cannot protect user privacy against an untrusted aggregator in mobile sensing applications. Several recent works consider the aggregation of time-series data in the presence of an untrusted aggregator. To protect user privacy, they design encryption schemes in which the aggregator can only decrypt the sum of all users’ data but nothing else.
Ø Use threshold Paillier cryptosystem to build such an encryption scheme. To decrypt the sum, their scheme needs an extra round of interaction between the aggregator and all users in every aggregation period, which means high communication cost and long delay. Moreover, it requires all users to be online until decryption is completed, which may not be practical in many mobile sensing scenarios due to user mobility and the heterogeneity of user connectivity.
DISADVANTAGES OF EXISTING SYSTEM:
Ø Cannot protect user privacy against untrusted aggregators.
Ø Existing works do not consider the Min of time-series data.
PROPOSED SYSTEM:
Ø We propose a new privacy-preserving protocol to obtain the Sum aggregate of time-series data.
Ø The protocol utilizes additive homomorphic encryption and a novel, HMAC- based key management technique to perform extremely efficient aggregation.
ADVANTAGES OF PROPOSED SYSTEM:
Ø Our scheme has much lower communication overhead than existing work.
Ø Utilizes the redundancy in security to reduce the communication cost for each join and leave.
MODULES:
] System Model Module
] Encryption Scheme Module
] Key Generation Module
] Aggregation Protocol Module
MODULES DESCRIPTION:
System Model Module
ü In this module first we develop our system model, with mobile users.
ü An aggregator wishes to get the aggregate statistics of n mobile users periodically, for example, in every hour.
ü The time periods are numbered as 1, 2, 3, . . . , and so on.
ü In every time period, each user i encrypts her data xi with key ki and sends the derived ciphertext to the aggregator. From the ciphertexts, the aggregator decrypts the needed aggregate statistics using her aggregator capability k0.
ü In each time period, a mobile user sends her encrypted data to the aggregator via WiFi, 3G or other available access networks. No peer-to-peer communication is required among mobile users, since such communication is nontrivial in mobile sensing scenarios due to the high mobility of users and users may not be aware of each other for privacy reasons.
ü We consider an untrusted aggregator that is curious about each individual user’s data. The aggregator may eavesdrop all the messages sent from/to every user. A number of users may collude with the aggregator, and reveal their data to the aggregator. A number of users may also collude to obtain the aggregate.
Encryption Scheme Module
ü One building block of our solution is the additive homomorphic encryption scheme. Encryption is the process of translating plain text data (plaintext) into something that appears to be random and meaningless (ciphertext). Decryption is the process of converting ciphertext back to plaintext.
ü To encrypt more than a small amount of data, symmetric encryption is used. A symmetric key is used during both the encryption and decryption processes. To decrypt a particular piece of ciphertext, the key that was used to encrypt the data must be used.
ü The goal of every encryption algorithm is to make it as difficult as possible to decrypt the generated ciphertext without using the key. If a really good encryption algorithm is used, there is no technique significantly better than methodically trying every possible key. For such an algorithm, the longer the key, the more difficult it is to decrypt a piece of ciphertext without possessing the key.
ü It is difficult to determine the quality of an encryption algorithm. Algorithms that look promising sometimes turn out to be very easy to break, given the proper attack. When selecting an encryption algorithm, it is a good idea to choose one that has been in use for several years and has successfully resisted all attacks.
Key Generation Module
ü Suppose there are nc random numbers. The aggregator has access to all the numbers, and it computes the sum of these numbers as the decryption key k0. These numbers are divided into n random disjoint subsets, each of size c. These n subsets are assigned to the n users, where each user has access to one subset of numbers. User i computes the sum of the numbers assigned to it as the encryption key ki.
ü The aggregator cannot know any user’s encryption key because it does not know the mapping between the random numbers and the users. When c is large enough, it is infeasible for the aggregator to guess the numbers assigned to a particular user with a brute-force method.
ü The aggregator’s decryption key cannot be revealed by any user because no user knows all the numbers.
Aggregation Protocol Module
ü The Min aggregate is defined as the minimum value of the users’ data. This module presents a protocol that employs the Sum aggregate to get Min.
ü Each user uses just one set of secrets for all instances of the sum aggregation protocol.
ü When the plaintext space is large, the cost of the basic scheme is high. In some application scenarios, it may not be necessary to get the exact Min, but an approximate answer is good enough. For such scenarios, the basic scheme can be extended to get an approximate Min with much smaller cost.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Ø System                          :         Pentium IV 2.4 GHz.
Ø Hard Disk                      :         40 GB.
Ø Floppy Drive                 :         1.44 Mb.
Ø Monitor                         :         15 VGA Colour.
Ø Mouse                            :         Logitech.
Ø Ram                               :         512 Mb.
SOFTWARE REQUIREMENTS:
Ø Operating system   :         Windows XP/7.
Ø Coding Language  :         JAVA
Ø IDE                            :         Netbeans 7.4
Ø Database                 :         MYSQL

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