STEGANOGRAPHIC APPROACH TO ENSURE DATA STORAGESECURITY IN CLOUD COMPUTING USING HUFFMAN CODING1 CREATED BY: HASIMSHAH . R . S
CONTENTS1.INTRODUCTION2.RELATED WORK3.DESIGN OF THE SYSTEM4.ALGORITHMS USED IN THE SYSTEM5.SECURITY ANALYSIS AND PERFOMANCE EVALUATION6.CONCLUSION7.REFERENCES
INTRODUCTION Cloud computing
The cloud computing model allows access to information and computer resources from anywhere that a network connection is available. Cloud computing provides a shared pool of resources, including data storage space, networks, computer processing power, and specialized corporate and user applications.
Cloud computing is a practical approach to experience direct cost benefits and it has the potential to transform a data center from a capital-intensive set up to a variable priced environment. The idea of cloud computing is based on a very fundamental principal of reusability of IT capabilities'. 4
INTRODUCTION(CNTD)5Data securityCant implement Traditional cryptographic technology .Cloud - not a third-party warehouse.Data stored in multiple physical locations in random mannerSteganographic Approach Using Huffman Coding ensures explicit dynamic data support security of data when these data are in the cloud storage.
INTRODUCTION(CONT)The Huffman Tree constructs an optimal prefix code called a Huffman code.Lets say, there are six characters A,B,C,D,E and F as shown in Fig a .
Now for a given code 0 100 100 1101 we can decode them to get back the original code by traversing the Huffman tree.
CLOUD COMPUTING ARCHITECTURE AND SECURITY ISSUESDEPLOYMENT MODELSPrivate cloudCommunity cloudPublic cloudHybrid cloudSERVICE DELIVERY MODELSSoftware as a Service(SaaS)Platform as a Service(PaaS)Infrastructure as a Srevice(IaaS)8INTRODUCTION(CONT)
9Phishing data loss botnet (Collection of machines are running remotely).botnet - offers more reliable infrastructure at a relatively low price for attack.INTRODUCTION(CONT)
Problem statement :Main problem - loss of control of data stored in the cloud.10
Schematic System Architecture for CloudINTRODUCTION(CONT)
RELATED WORKcong wang et al.use homomorphic token with distributed verification of erasure-coded data.but it is failed to achieve public verifiability and storage correctness.
shantanu pal et al. ensures to find location of adversary or the attacking party from its target.it may try to attack them, if adversary knows the location of the other vms. this may harm the other vms in between.
ateniese et al.proposed the provable data possession (pdp) model to ensure possession of file in untrusted storages.This scheme used public key based homomorphic tags to audit the data file and it is providing public verifiability.11
DESIGN OF THE SYSTEMStoring data into some images. - steganography .12Processes to store or retrieve their data :
Computational model to Store Data
Computational model to Retrieve Data
Human Visual System(HVS) has very low sensitivity.
Variable length encoding doesnt help attacker to recognize characters.
He/she has no idea about frequency of characters.
Cant generate Huffman code.
Ultimately we are having a secured system
Image databaseImages stored in CSP-1Set of images sent to CSP-3 - user wants to store data in cloudFile databaseFile holds the address of imagesEmbedded data into ImagesCounts total no. of charactersFinds frequency of each characters by Huffman codeApplies Steganography to both frequency of characters & codified data.
ALGORITHMS USED IN THE SYSTEMALGORITHM 1 : HF-codification()1. procedure2. Read file FText which is to be saved in Cloud3. Compute CN from FText4. Find the frequency of occurrences of each characters in Ftext and store them in some chronological order5. Store frequency in a new file FFreq .6. FN = Freq-Codification( )16
177. Call Huffman-Tree()8. Create a file FCode9. Open FN. Reach EOF of FN where the originalcharacters of FText will be replaced by the Huffmancodes present in FCode .10. Calculate the total Bit BCount in FN.11. Delete FText, F Freq and F Code.12. Call Steganography() to perform steganography on FN13. end procedure
FILE CODIFICATIONfrequency file is read digit by digit & each digit is codified into 4- bit binary patternAlgorithm 2: Freq-Codification ()1. procedure2. Open FFreq and a new File FN.3. while ( Read characters from F Freq until EOF )4. do if (character is a new line character)5. Append 1111 at the end of FN.6. else18
197. Convert the digit to its 4-bit binary form.8. Append those 4-bits at the end of FN.9. end if10. end while11. Append 11111111 at the end of FN.12. Return FN13. end procedureAlgorithm 2: Freq-Codification () (CNTD)
Hiding Data within Images SteganographyDeals the pre-requisite requirements like :load image, store file name, image indexfinally call the MdfImg operation which will map data from file to images.ALGORITHM 3: STEGANOGRAPHY()1. procedure2. Load Image_Index = ImageSearch (Image_Database)3. Store (FName, BCount, Image_Index)4. MdfImg (Image_Database [Image_Index]);5. end procedure.20
SEARCHING OF VALID IMAGEThe algorithm searches an image which we can be used to store the data. It returns the address of a valid image if it is available in image database.ALGORITHM 4: IMAGESEARCH(IMAGE_DATABASE)1. procedure2. Open Image_Database;3. for Image_Database(i), i