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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566 A Novel Approach For Privacy Preserving Videosharing And Merging Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G Department of IT, MVGR College of Engineering, Vizianagaram, AP, IndiaAbstract In present days rapid growth of internet preserving data mining. A privacy-preserving datahas paved a path for increased utilization of mining technique must ensure thatdistributed applications. The number of any information which is disclosed ―cannot beapplications aredrastically increased for a traced to an individual‖ or ―does not constitute andistribution of video information to various intrusion ―.places. In recent years, videos are also An improvement in our knowledge aboutplayingmajor role at various surveillance an individual could be considered an intrusion. Theapplications. Hence, propose a novel approach to latter is particularly likely to cause a problem forshare the videos to various places while providing data mining, as the goal is to improve ourprivacy.In this paper we used an efficient knowledge. Even though the target is often groupsalgorithm to merge the given video. This method of individuals, knowing more about a group doesprovides various parameters to preserve the increase our knowledge about individuals in theprivacy and accuracy. Our proposed framework group. This means we need to measure both theis highly efficient than various existing knowledge gained and our ability to relate it to aapproaches like Smart Cameras, Homomorphic particular individual, and determine if these exceedEncryption and Secure Multi-Party computation the thresholds.Privacy, therefore, happens to beto carry out privacy preserving video serious concern in the age of video surveillance.surveillance. This work opens up a new avenue Widespread usage of surveillance cameras [3], infor practical and provably secure offices and other business establishments, pose aimplementations of vision algorithms. The significant threat to the privacy of the employeesproposed system along with motion segmentation and visitors. It raises the specter of an invasive `Bigresults will be used to detect and track peoples or Brother society. In this regards, certain privacy lawsobjects. have been introduced to guard an individual‗s privacy/rights. Despite these, video surveillanceIndex Terms-Privacy, Video Surveillance, remains vulnerable to abuse by unscrupulousSharing algorithm, Merging Algorithm, Vision operators with criminal or voyeuristic aims and toalgorithms institutional abuse for incriminatory purposes. These legitimate concerns frequently slow theI. INTRODUCTION deployment of surveillance systems. The challenge The Growth of internet has increased the of introducing privacy and security in such ausage and distribution of multimedia content among practical surveillance system has been stifled byremote locations.In present internet form, lack of enormous computational and communicationsecurity is observed while distributing the overhead required by the solutions.multimedia information.But security of sensitive The privacy of the system is based oninformation [1] is of primary concern in the field of splitting the information present in an image intocommercial,medical, military systems and even at multiple shares. The parameters used for shatteringwork places.Privacy plays a major role in internet are primes p1,p2,p3……pi and scale factor ‗sc‘ areapplications. Privacy is pertains to data is "freedom constant for each shattering operation and are infrom unauthorized intrusion". With respect to general assumed to be public. The only possibleprivacy-preserving data mining [2]. If users have information leakage of the secret is that retained bygiven the authorization to use data for the particular each share. We now analytically show that with andata mining task, then there is no privacy issue. And optimal parameter selection, the informationalso the user is not authorized, what user constitutes retained by a share is negligible.Information privacy―intrusion‖ A common standard among most is concerned with preserving the confidentiality ofprivacy laws (Ex-European Community privacy information and is therefore the most relevant kindguidelines or the U.S. healthcare laws) is that of privacy with respect to the internet and emailprivacy only applies to ―individually identifiable monitoring or electronic monitoring [3]. Therefore,data‖. By combining intrusion and individually it is essential to develop an efficient method whichidentifiable leads to a standard to judge privacy can ensure that the data is not tampered. Though encryption techniques are popular and assures the 560 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566integrity and secrecy of information, single point security co-exists in the domain of computer vision.failure is the major vulnerability [3] for large We exploit the following facts, which are valid forinformation (like satellite photos,medical images or most of the computer vision tasks. Meaningfuleven video information) contents. Privacy in the images from real-world have following propertiesworkplace is a very important issue that can cause that are of interest to us.some controversy. There are certain privacy lawsthat are designed to protect employees and when Limited and fixed Rangeusing video surveillance, privacy of employees must The values that an algorithm can take arebe considered [4]. Privacy issues on video finite and are from a limited fixed range. And moresurveillance cameras are most likely to occur when importantly the range is known aprior [5]. Thus, thethe employees do not realize that they are being algorithms that have multiple possible answers, butfilmed (covert video surveillance) or when cameras only one of them within this possible range, are asare located in places where people do not want to be valid as solutions that have only one answer. Suchfilmed. There are also concerns that, video algorithms need not be useful (or correct) for asurveillance (particularly covert video surveillance) general purpose (non-vision) tasks. In this approach,may unfairly target certain minority groups. we have exploited this by designing efficient, secure Video surveillance is a critical tool for a surveillance solutions that has infinite answers, butvariety of tasks such as law enforcement, personal only one of them in the valid range. Interestingly, insafety, traffic control, resource planning, and this process, we I also circumvent Oblivioussecurity of assets, to name a few. Rapid Transfer, thereby gainingin efficiency.development/deployment of closed circuit television(CCTV) technology plays a key role in observing Scale Invariancesuspicious behavior. However, the proliferation in The information in the image remainsthe use of cameras for surveillance has introduced practically unchanged even if we change the units ofsevere concerns of privacy. Everyone is constantly measurement or scale the whole data. This is notbeing watched on the roads, offices, supermarkets, true for most non-image information. We exploitparking lots, airports, or any other commercial this to design a wrapper algorithm which converts aestablishment. This raises concerns such as, partially secure algorithm to a completely securewatching you in your private moments, locating you one. For example, consider a partially secureat a specific place and time or with a person, spying algorithm that may reveal the LSB of all the pixels.on your everyday activities, or even implicitly Suppose this algorithm is run on an input which iscontrolling some of your actions. Advantage of scaled (at least by a factor of two) with all the LSBsvideo surveillance is it keeps track of video randomized. Then note that with practically noinformation for future use and is helpful in change in the output, the original input (beforeidentifying people in the crime scenes etc. scaling) is completely secure. Keeping in mind theDisadvantage of the present system is that it is above properties, this design is a vision specific,difficult to maintain heavy amount of raw video distributed framework for surveillance tasks thatdata and Human interaction. This requires higher preserves privacy. The emphasis is on performingbandwidth for transmitting the visual data. this efficiently, thus facilitating proactive Privacy preserving video surveillance surveillance. In this framework, each video isaddresses these contrasting requirements of shattered into shares and each one is sent to aconfidentiality and utility. The objective is to allow different site in such a way that each site has nothe general surveillance to continue, without information about the scene (data-specific secretdisrupting the privacy of an individual. This novel sharing). The complete algorithm runs in thistechnology addresses the critical issue of ―privacy distributed setup, such that at the end of theinvasionin an efficient and cost-effective way. protocol, all that the observer recovers is the finalPrivacy concerns often prevent multiple competitive output from the results obtained at each of theorganizations from sharing and integrating data site.The extent to which employers can use videotaken from various videos. In traditional approaches, surveillance to monitor employees may depend onvarious algorithms are developed to prevent privacy the state they are living in and the type of businessin some extent. But in privacy preserving video they are conducting. In most cases, employeessurveillance uses the secret sharing technique to should be notified of the video surveillance that isachieve complete privacy and efficient computation taking place and the video surveillance should notof surveillance algorithms. include areas where employees have a right to expect some privacy. In some cases, covert videoRole of Visual Data surveillance is allowed (for instance, when an While general purpose secure computation employee is suspected of criminal activity), but anappears to be inherently complex and oftentimes employer may need to get permission from theimpractical, we show that due to certain "suitable" relevant authorities before implementing this.properties of visual information, efficiency and General video surveillance for security purposes 561 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566will, in most cases, not be considered to breach any decrypted by the corresponding private key. Theseprivacy rights in the workplace.Video surveillance is keys are related mathematically, but the private keynot the only type of monitoring that may be cannot be practically derived from the public key.conducted in the workplace. In practice, PKC can be used to ensure Thus, to resolve these problems, secret confidentiality of the data. The messages encryptedsharing schemes have been proposed based on with a recipient‗s public key can only be decryptedthreshold in 1979. Secret Sharing refers to a method using the corresponding private key. The private keyfor distributing a secret among a group of servers of which is known only to the intended receiver.each of which is allocated a share of the secret [6]. Asymmetric key algorithms are generally found toThe secret can be reconstructed only when the be computationally expensive. Some ofthe popularshares are combined together; on their own, the PKC algorithms include Pailliers and El-Gamal.individual shares give no information of the secret.Several types of secret sharing schemes have been Secure Multi-Party Computation(SMC)proposed in literature. Shamirs secret sharing In cryptography, secure multi-partyscheme [6] represents the secret as the y-intercept of computation or secure computation or multi-partyan n-degree polynomial, and shares correspond to computation (MPC) is a problem that was initiallypoints on the polynomial. In contrast, Blakleys suggested by Andrew C. Yao [5] in 1982 paper. Inscheme specifies the secret as a point in n- that publication, the millionaire problem wasdimensional space [3], and gives out shares that introduced: Alice and Bob two millionaires whocorrespond to hyper planes that intersect the secret want to find out who is richer without revealing thepoint. The primary motivation behind Secret precise amount of their wealth. Yao proposed aSharing is of securing a secret over multiple servers. solution allowing Alice and Bob to satisfy theirHowever, computing functions on the input secretly curiosity while respecting the constraints. Theshared among n-servers requires highly concept is important in the field of cryptography andcommunication intensive protocols (which relies on is closely related to the idea of zero-knowledgeness.some sort of SMC). Furthermore, such schemes In general, it refers to computationalresult in huge data expansion, which becomes in- systems in which multiple parties wish to jointlyefficient for large secrets, such as live-videos (as in compute some value based on individually heldour case). For example, Shamirs shares are each as secret bits of information, but do not wish to reveallarge as the original secret, where as Blakleys their secrets to one another in the process. Forscheme is even less space-efficient than Shamirs. example, two individuals who each possess someEach secret share is a plane, and the secret is the secret information—x and y, respectively—maypoint at which three shares intersect. Two shares wish to jointly compute some function f(x,y)yield only a line intersection. without revealing any information about x and yThe primary motivation is proposing a paradigm other than can be reasonably deduced by knowingshift for our real-timetasks was to reduce the actual value of f(x,y), where "reasonablycommunication. We next show that visual-data has deduced" is often interpreted as equivalent tocertain characteristic properties, which can be computation within polynomial time.exploited to define a tailor made Secret Sharing The primary motivation for studyingscheme exclusively for visual data. Compared to the methods of secure computation is to design systemsstandard CRT based Secret Sharing schemes, our that allow for maximum utility of informationscheme significantly reduces the data expansion by without compromising user privacy. SMC usesat least a factor of,where is the number of interactions between multiple parties to achieve aservers/shares. Such reduction is prominent for huge specific task, while keeping everyone oblivious ofdata such as live-video feeds, thus making privacy others data. Introducing privacy and security inpreserving video surveillance practical. visual data processing was attempted with considerable success in different domains. BlindVideo surveillance techniques Public Key vision [7] allows someone to run their classifier onEncryption (PKC) another person‗s data without revealing the The process of converting the plaintext (P) algorithm or gaining knowledge of the data.to cipher text (C) using an algorithm is called Shashank exploited the clustered nature of imageencryption (E). On the other hand, restoring the databases to improve the efficiency of SMC forplaintext from the cipher text is called decryption example based image retrieval by processing(D). Public key Encryption (PKC), also known as multiple queries together.asymmetric cryptography, is a form of cryptographyin which key used to encrypt a message differs from Smart Camerasthe key used to decrypt it. Private Key is kept secret, Smart cameras do surveillance in thewhile the public key can be widely distributed. The camera itself or try to mask sensitive information inmessage that needs to be conveyed to the recipient is the videos. The former requires expensiveencrypted using his public key. It can only be programmable cameras and are restricted to single 562 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566camera algorithms. Changing the algorithms is organized as follows, Section-I describes thetedious and costly. The second approach addresses procedure to preserve the privacy before secretproblem specific concerns in surveillance videos. distribution andSection-II explains the PrivacyFace swapping and face de-identification [8] try to preserving technique using sharing algorithm andmodify face images such that they can be Merging algorithm section-III describestheautomatically detected, but cannot be correctly ExperimentalResults and finally, Section-IVrecognized. Face detection techniques uses concludes the paper.programmable cameras to carry out detection and Privacy is a major concern in videomasking of the regions of interest. transmission. Before transmission of video from one All the above approaches rely on the place to another,it is essential to change the form ofsuccess of detection of interest regions and do not the information to provide security to theprovide any guarantee of privacy. Moreover, the information which is to be transmitted. Therefore, tooriginal video is lost in all. We note that most of the attain this process, consider a video file (.avi orcurrent privacy preserving algorithms are based on .mpg) and split into number of frames. Select one ofthe generic framework of SMC requires heavy the frames F from video V. For enabling thecommunication to achieve secure computation. For distributed secured processing, frame F isinstance, a single multiplication is carried out via distributed to N number of parties.If frame F iscomplex distributed protocol involving oblivious directly distributed,there is a chance of informationtransfer [9], which is a highly communication leakage.Hence, to avoid information leakage, weintensive subroutine in SMC. need to maintain privacy to the input frame.In this Factually, the round-trip time in a LAN is process, scaling (scale the positive integer with eachof the order of a few milliseconds, whereas several pixel) and randomization(generation of randomfloating operations take no more than few number and summation with each pixel) are appliednanoseconds. Clearly, these delays are too high, to the frame. After scaling and randomization, thewhile dealing with voluminous data like frame is processed for secret sharing.surveillance videos. Hence, solutions based on SMCare impractical for our application. In this work, the II.PRIVACYPRESERVINGTECHNIQUEparadigm of secret sharing is to achieve private and USING SHARING ALGORITHM ANDefficient computation of surveillance algorithms MERGING ALGORITHM[10]. Secret sharing (SS) methods [11,12] try to split Input: Capturing a video from camera andany data into multiple shares such that no share by spilt into frames.itself has any useful information, but together, Step1: Initially, choose N number of relativelythey retain all the information of the original data. primes( gcd(I,J)=1 the I and J are relatively prime)However, the standard SS methods, which were where N equal to number of shares.invented to address secure storage of data, results in Step2: Apply the Scaling (Scale the pixels withsignificant data expansion (each share is at least the fixed integer i.e.,sc) and Randomization (addingsize of the data). randomvalues to each pixel) for ensuring accuracy Computing on the shares is inefficient as it before sharing algorithm.would require some sort of SMC. In this work, we To obtain the shares from the secret S asexploit certain desirable properties of visual data share1= S mod 𝑝1 ,such as fixed range and insensitivity to data scale, to share2= S mod 𝑝2 ,achieve distributed efficient and secure computation share3=S mod 𝑝3of surveillance algorithms in the above framework.This approach also addresses the concerns related to shareN=S mod 𝑝 𝑁 ,video surveillance, presented below. To achieve where, 𝑃1 , 𝑃2 , … . 𝑃 𝑁 𝑎𝑟𝑒𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒𝑙𝑦𝑝𝑟𝑖𝑚𝑒.distributed secure processing and storage and also Step3: Each share is sent to individualaddress these issues more effectively we have computational severs and apply the affinedeveloped a new system to supporting privacy transformation on each individual computationalpreserving video surveillance. server. But all these techniques have various Step4: Affine Transformation on share1 as 𝑝. 𝑑 𝑖 +disadvantages such as increase in share size,poor 𝑞.𝑠𝑐𝑚𝑜𝑑𝑝1, where p,q are fixed values which arecontrast ratio in the reconstructed image or other used for privacy and di is pixel values of share1 andissues related to security before computation of the sc is scaling factor.image. Step5: Affine Transformation is applied to each Hence, to overcome these drawbacks, a independent server and keeps results for obtainingnew approach is proposed using sharing and original result.Merging algorithms, which are more efficient thanthe other techniques. In this approach, we have MERGING ALGORITHMutilized Chinese remainder theorem for mergingvarious shares into a secret. The rest of the paper is 563 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566 We need to show that a solution exists and We havethat is unique modulo M. 880*N1 1 mod 3, 1. To construct a simultaneous solution, Let 520*N2 1 mod 5, Mi= M / mi,i=1,2,3,…………………,n. 240*N3 1 mod 11, Where, Mi is the product of moduli except for ithterm. 165*N4 1 mod 16. 2. In this, GCD(M/mi,mi)=1. Using Extended Solving using the Extended Euclidean algorithm, Euclidean algorithm we have N1 = 1, N2 = 2, N3 = 5, N4 = -3. 3. We can find Ni such that M/mi*Ni 1 Therefore, x = 2*880*N1 + 3*528*N2 + mod mi Then, 4*240*N3 + 5*165*N4 4. X a1*(M/m1)*N1+ a2*(M/m2)*N2+ = 2*880*1 + 3*528*2 + 4*240*5 + 5*165*(-3) ………+ ar*(M/mr)*Nr. = 7253 (mod 2640) = 1973. 5. Therefore, X ai*{M/mi}*Ni We have x = 1973 as a common solution to Rest of the terms yield the result to a value the above system of congruence. All other solutionszero, Since M/mj 0 mod mi, when i≠j. X satisfies are of the form 1973+ M*i,i=1, 2, 3 . . . and so on.all the congruencies in the system. X is the uniquesolution for modulo M.Using Merging Algorithm to solve III. EXPERIMENTAL RESULTS X 2 mod 3, In this process, consider any video file(eg: walk.avi), split into number of frames. Choose any X 3 mod 5, input frame and apply scaling & randomization to X 4 mod 11, preserve the privacy. Now, Scaled and RandomizedX 5 mod 16. image doesn‘t reveal any useful information. Hence, Clearly, the moduli are relatively prime in pairs. using the secret sharing technique, we can achieveM= 3*5*11*16 = 2640. the efficient privacy preserving process. Figure 1.c,M1 = 2640/3 = 880,M2 = 2640/5 = 528, M3 = Figure 1.d and Figure 1.e are the individual shares2640/11 = 240, M4 = 2640/16 = 165. which are sent to the computational servers.Figure 1.:(a)Input Frame taken from video. (b) After Scaling and Randomization (c)Share1 of the input frame(d)Share2 of the input frame (e)Share3 of the input Frame (f)Transformed Share1 of the input frame 564 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566Figure 2: (a)Transformed Share2 (b)Transformed Share3 (c) Reconstructed Frame In this example, we used three computational server. The correspondingcomputational servers to perform the Transformed shares are shown in Figuretransformation of shares. There, we applied 2.Finally,Observer will merge these threetransformation formula (p.di+q.SC) mod pi, here transformed shares using efficient sharing andp,q are positive integers that are used in merging algorithms.Our Transformation will givereconstruction phase (for retrieving original loss-less result in less time.image). Here, di is the pixel values of the shares In this section, we also presented few otherand SC is the positive scale factor and pi is the experimental results where sharing and Mergingprime number of the corresponding shares. Affine algorithms applied.transformation is independent of eachFigure 3.:(a)Input Frame taken from video. (b)Frame After Scaling.(c)After Scaling and Randomization(d)Share1 of the input frame (e)Share2 of the input Frame (f)Share3 of the input frameFigure 4: (a)Transformed Share1 (b)Transformed Share2 (c)Transformed Share3(d) Reconstructed Frame 565 | P a g e
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Anjanadevi B,Nagesh Vadaparthi, Jyothi V,Satyanarayana Reddy G/ International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.560-566 The merging algorithm is applied on S. Yu. Online available at 3different types of videos and the PSNR values are http://www.charuaggarwal.net/toc.pdf. calculated with various scaling factors. The table-1 [3] ManeeshUpmanyu, Anoop M. below shows the PSNR values for different scaling Namboodiri, KannanSrinathan and C.V. factors. Jawahar, ―Efficient Privacy Preserving TABLE 1:PEAK SIGNAL – TO – NOISE – RATIO Video Surveillance‖, In Twelfth Scaling Factor International Conference on Computer Image resolution Vision (ICCV), 2009. 33 80 120 [4] Hazel Oliner, ―Email and internal DFS.AVI(320 x 240) 56.624 99 99 monitoring in the Workplace: Information DELTA.MPEG (320x200) 53.8 99 99 Privacy and Contracting out‖, Industrial Law Journal 321 – 322, (2002) 31 (4). WALK.AVI (320 x 240) 55.025 99 99 [5] A. C. Yao. ―Protocols for secure computations‖.InProc. 23rd IEEE Symp.on Foundations of Comp. Science, pages 160–164, Chicago, 1982. IEEE.120 [6] C.C. Thien and J.C. Lin, ―Secret image100 sharing," Computers & Graphics, vol. 26, no. 5, pp.765 - 770,2002. 80 [7] S. Avidan and M. Butman. ―Blind vision‖. 60 InProc. of European Conference on 40 Computer Vision, 2006. Series1 [8] Model-based Face de-idenfication 20 Series2 technique is online available at 0 ieeexplore.ieee.org/xpls/abs_all.jsp?arnum Series3 ber=1640608 [9] C Narasimha Raju, GanugulaUmadevi, KannamSrinathan and C V Jawahar, ―A Novel Video Encryption Technique Based on Secret Sharing‖, ICIP 2008. [10] B.Anjanadevi, P Sitharama raj,V Jyothi,V,Valli Kumari. A Novel approach for Privacy Preserving in Video using Extended Euclidean algorithm Based on Chinese remainder theorem. International IV. CONCLUSION Journal Communication & Network In this approach, we obtained the results in Security (IJCNS), Volume-I, Issue-II, much effective manner and also found that the 2011. computational time is less when compared to the [11] J. C. Benaloh. Secret sharing other techniques. It is also observed that the size of homomorphisms: keeping shares of a the output image file is almost equal to the original secret secret. CRYPTO, 283:251–260, size where in other techniques, it is found that the 1986. output file size is larger than the original one. [12] Craig Gentry. Fully homomorphic Hence, our approach is more efficient than encryption using ideal lattices. STOC, others.This approach opens up a new avenue for pages 169–178, 2009. practical and provably secure implementations of various vision algorithms where distribution of data is over multiple computers. These results as guidelines along with motion segmentation can detect and track peoples. REFERENCES [1] G. Blakley, ―Safeguarding cryptographic keys," presented at the Proceedings of the AFIPS 1979National Computer Conference, vol. 48, Arlington, VA, June 1997, pp. 313 - 317. [2] An Introduction to Privacy-Preserving Data Mining Charu C. Aggarwal, Philip 566 | P a g e
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