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Scientific Journal of Impact Factor (SJIF): 5.71

e-ISSN (O): 2348-4470 p-ISSN (P): 2348-6406

International Journal of Advance Engineering and Research Development Volume 5, Issue 03, March -2018

Privacy And Secure Authentication Using Cooperative Query Answer Mrs. R.Salini , M.Tech (Asst.professor) V.Thaaraheswari, R.Thendral, S. Sharmila Department of Computer Science Engineering Panimalar Engineering College Chennai-600123, Tamil Nadu , India Abstract--Many web applications provide secondary authentication methods, i.e., secret queries, recovery mail , to reset user password when login fails. However, the answers to many such secret questions can be easily guessed by an acquaintance or exposed to a stranger that has access to public online tools; moreover, creating the secret queries after long a user may forget her/his answers. Today’s prevalence of smartphones has granted us new chances to observe and understand how the personal data collected by smartphone sensors and apps can help create personalized secret queries without violating the users’ privacy concerns. , In my project, provide a Secret-Q based security system, it’s called as “Secret-QA”, that creates a set of secret queries from the user smartphone usage. I develop a model on Android smartphones, and evaluate the security of the secret queries by asking the acquaintance/unknown person who participates in our user study to guess the answers with and without the help of online tools; meanwhile, we observe the queries’ by asking participants to answer their own queries. Our experimental results reveal that the secret queries related to motion sensors, calendar, app installment, and part of legacy app usage history (e.g., phone calls) have the best memorability for users as well as the highest robustness to attacks. Keywords—Android, Server, gyroscope sensor, I.


SECRET queries (a.k.a password recovery queries) have been widely used by many web applications as the secondary authentication method for resetting the account password when the primary credential is lost. When creating an online account, a user may be required to choose a secret question from a pre-determined list provided by the server, and set answers accordingly. The user can reset his account password by providing the correct answers to the secret queries later. For the ease of setting and memorizing the answers, most secret queries are blank-fillings (a.k.a. fill-in-the-blank, or short-answer queries), and are created based on the long term knowledge of a user’s personal history that may not change over months/years (e.g., “What’s the model of your first car?”). However, existing research has revealed that such blank-filling queries created upon the user’s long term history may lead to poor security and reliability. The “security” of a secret question depends on the validity of a hidden assumption: A user’s long-term personal history/information is only known by the user himself. However, this assumption does not hold when a user’s personal information can be acquired by an acquaintance, or by a unknown person with access to public user profiles. An acquaintance of a user can easily infer the answers to the user’s secret queries (e.g., “name of pet”). Moreover, an unknown person can figure out the answers from public user profiles in like face book or online social networks or search engine results (e.g., “the hospital your youngest child was born in”). The “reliability” of a secret question is its memorability—the required effort or difficulty of memorizing the correct answer. The recent prevalence of smartphone has provided a rich source of the user’s personal data related to the knowledge of his shortterm history, i.e., the data collected by the smartphone sensors and apps. Is it feasible to use the knowledge of one’s shortterm personal history (typically within one month) for creating his secret question? . he may wrongly spell the input that requires the perfectly matching to the correct value. Naturally, the transient individual history is less inclined to be presented to an obscure individual or associate, in light of the fact that the fast varieties of an occasion that a man includes experienced inside a here and now will expand the strength to figure assaults. This suggests enhanced security for such mystery questions. Also, look into discoveries in brain research demonstrate that one can without much of a stretch retain the subtle elements of his fleeting movement, if this action happens different circumstances amid a here and now (e.g., calling a companion ordinarily), as well as this action intensely includes his chance and exertion in a brief timeframe period (e.g., running activity). In the interim, we build up a model of Secret-QA, and lead a test client contemplate including 88 volunteers to assess the dependability and security of the arrangement of mystery question made in the framework. . In particular, I outline a client security framework with an arrangement of mystery questions made in light of the information of clients' here and now cell phone utilization. We assessed the unwavering quality and security of the three kinds of mystery queries with an exhaustive analysis including 88 members. The past down to earth comes about demonstrate that the blend of numerous genuine false and different pick questions require less information exertion with the comparative quality gave by clear filling @IJAERD-2018, All rights Reserved


International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 inquiries. We assess the ease of use of the framework, and find that the Secret-QA framework is less demanding to use than those current security framework with mystery questions in light of clients' long haul noteworthy information. we give a diagram of the framework plan, we assess the framework execution over all made mystery questions II.


The clear filling mystery questions are prevailing as the standard validation arrangement, particularly in web and email validation frameworks , notwithstanding the feedback on its security also, unwavering quality. Speculating Attacks by Acquaintance and Stranger. The security of mystery inquiries for verification was examined by Zviran and Haga in 1990 , which demonstrated that the answers of 33 percent inquiries can be speculated by the "critical others" who were primarily members' mates (77 percent) and dear companions (17 percent). Another comparative ponder was led by Podd et al, which uncovered a higher rate of effective speculating (39.5 percent) . A current report demonstrated that even an open inquiry composed by the client himself was as yet helpless against the speculating assaults propelled by his associate. Then again, outsiders can be more complex than at any other time to dispatch the speculating assaults, as they can get to the client's close to home history through online interpersonal organizations (OSN) or other open online instruments. Hence, the factual speculating has turned into a successful method to bargain a couple individual "mystery" questions [5] (e.g., "Where were you conceived?", "What is the name of your high school?"). Poor Reliability of Secret Questions in Real World. Concerning the unwavering quality, a mystery question ought to be memory-wise easy for clients . Be that as it may, the present standard mystery question techniques neglect to meet this necessity. A current report uncovered that almost 20 percent clients of four well known webmail suppliers overlooked their answers inside a half year. Additionally, prevailing clear filling mystery inquiries with case touchy answers require the ideal truly coordinating to the set reply, which likewise adds to its poor dependability. Late Proposals of User Authentication Systems. To lessen the powerlessness to speculating assaults, Basic et al had a go at utilizing here and now data, for example, a client's dynamic Internet exercises for making his mystery questions, to be specific system exercises (e.g., perusing history), physical occasions (e.g., arranged gatherings, timetable things), and calculated conclusions (e.g., conclusions got from perusing, messages). They stressed that much of the time changing mystery inquiries will be troublesome for aggressors to figure the appropriate responses. Be that as it may, this exploration depends on the information identified with a client's Internet exercises, while our work use the cell phone sensor furthermore, application information that can record a client's physical world exercises, for making mystery questions. For better dependability, one may pick different kinds of mystery addresses instead of clear filling inquiries to maintain a strategic distance from the trouble in reviewing and contributing the ideal literally matching reply. For instance, the login to an online social arrange requires a client to remember one of his companions in a photograph . Notwithstanding, it is possible that a client neglects to perceive on the off chance that he isn't well-known to that specific companion picked by the validation server. Such existing proposition fill in as a decent beginning of utilizing one's here and now exercises to make mystery inquiries and in addition attempting other inquiry composes. Since the cell phone has moved toward becoming one's most indistinguishable gadget of recording his life, this paper presents a client verification framework Secret-QA to examine on how one's fleeting history—a wide range of one's exercises sensible to the cell phone—can profit the security and dependability of mystery questions. Then, we assess the assault power of utilizing a blend of numerous lightweight questions (genuine/false, numerous decision) rather than utilizing the clear fillings, keeping in mind the end goal to strike an adjusted tradeoff between security(and/or unwavering quality) and ease of use. III SYSTEM OVERVIEW 3.1 User Extraction System The present cell phones are ordinarily furnished with a plenty of sensors and applications which can catch different occasions identified with a client's every day exercises, e.g., the accelerometer can record the client's games/movement status without devouring over the top battery . Choice of Sensors/Apps. In the client occasion extraction conspire, Secret-QA chooses an arrangements of sensors and applications for removing the client exercises, including: (1) the normal sensors prepared on the main ten top of the line cell phones in 2013, (2) the best ten downloaded Android applications in 2013, and (3) the inheritance applications (Call, Contact, SMS, and so forth.), as appeared in Table 1. Since these sensors and applications are now built in for all the cell phones, our approach is normally reasonable for cell phone clients without presenting any additional equipment costs. The present cell phones are ordinarily furnished with a plenty of sensors and applications which can catch different occasions identified with a client's every day exercises, e.g., the accelerometer can record the client's games/movement status without devouring over the top battery . Choice of Sensors/Apps. In the client occasion extraction conspire, Secret-QA chooses an arrangements of sensors and applications for removing the client exercises, including: (1) the normal sensors prepared on the main ten top of the line cell phones in 2013, (2) the best ten downloaded Android applications in 2013, and (3) the @IJAERD-2018, All rights Reserved


International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 inheritance applications (Call, Contact, SMS, and so forth.), as appeared in Table 1. Since these sensors and applications are now builtin for all the cell phones, our approach is normally reasonable for cell phone clients without presenting any additional equipment costs.

Fig. 1. System architecture of Secret-QA, for a typical user scenario of resetting the account password through answering the secret questions In fact, the main test comprises in transmitting power (covering the separations amongst maker and customer). Amid this transmission procedure, protectors assume an essential part by keeping up electrical protection running from appropriation to transmission lines and supporting mechanical load between a transmitter and the ground. Experiments were built up on a plane protecting surface for better deceivability of showing up releases. The utilization of morphological sifting, through disintegration then widening brings about wiping out commotions on pictures before any further processing. The diminishing of N speaks to a vital outcome and demonstrates that the quantity of releases diminishes. Pixels are plainly obvious on the portioned picture and don't speak to any electrical releases on the separator surface. 3.2 Three Phase Challenge Protocol As appeared in Fig. 1 (from stage 1 – 5), a specialist organization needs to confirm the client's personality (normally to resetting the record secret key) through our put stock in server. The administration recommends three stages for validation. Issue: the client issues a confirmation demand to the specialist organization (e.g., an OSN site, the stage 1 in Fig. 1), at that point the OSN site approaches our trusted server for at least one scrambled mystery inquiries and its answers; the inquiries are at last exchanged to the client showing on the cell phones (the stage 2 – 3 in Fig. 1). The data at this stage must be sent over a protected channel against the noxious busybodies. Test: the client gives answers to the test inquiries as indicated by his/her transient memory, at that point sends it back to the OSN site (the stage 4 in Fig. 1). Confirmation: the validation is fruitful if the client's reaction fits in with the right answers; generally, a potential assault is distinguished. On the off chance that the seasons of confirmation disappointment surpasses the limit, our trusted server would deny to give administration to this specific client, as the in the last advance in Fig. 1. Note that the cooperations with server is likewise important to enhance the versatility to some conspicuous assault vectors in nearby task mode. For example, if a client's cell phone is stolen/lost (or the client has been trailed by an outsider for quite a long time), the client can cripple EvenLog usefulness (or remote bolt/swipe out the telephone) to dispose of the peril of potential enemy who records the clients' current exercises with the assistance of server. @IJAERD-2018, All rights Reserved


International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 3.3 Threat Models Previous investigations including concentrated on assaults propelled by clients' critical others or colleagues, however they overlooked pernicious speculating assaults from outsiders. Also, complex assailants could exploit online devices to build their figure rate. In this way, we consider danger models of the two above crossed elements (associate versus stranger; with versus without online instruments or outer help): (1) colleague assaults utilizing on the web devices, (2) associate assaults without outside help, (3) more unusual assaults utilizing on the web devices, (4) more interesting assaults without outer help. 4.Challenge Response Protocol We make three kinds of mystery addresses: A "Genuine/false" question is likewise called a "Yes/No" question since it ordinarily expects a double answer of "Yes" or "No"; a "different decision" question or a "clear filling" question that regularly begins by a letter of "W", e.g., Who/ Which/ When/ What (and in this way we call these two sorts of inquiries as "W" questions). We have two methods for making inquiries in either a "Yes/No" or a "W" organize: (1) a recurrence based inquiry like "Would someone say someone is (Who is) your most-visit contact in a week ago?"; Note that the mystery questions made in our framework are case addresses that we have for concentrate the advantages of utilizing cell phone sensor/application information to enhance the security and dependability of mystery questions. Specialists are allowed to make more mystery inquiries with new inquiry groups or by utilizing new sensor/application information, which prompts greater adaptability in the plan of an auxiliary validation instrument. 4.1 True/False Questions Location (GPS) Related Questions The illustration question identified with GPS is No. 1 "Did you leave grounds yesterday?". The GPS sensor catches the area data of the members so we could without much of a stretch learn whether members left grounds far sufficiently away with GPS organizes recorded. Since that the coarse-grained GPS information has an ordinary mean blunder of 500 meters as portrayed in Android API reference , and along these lines we decide a member leaves the grounds when the GPS area is 500 meters out of the grounds zone. Movement Activity (Accelerometer) Related Questions. The case question identified with accelerometer is No. 2 "Did you do running activity for no less than 10 min with your telephone conveyed yesterday?". There are numerous cell phone applications that assistance clients to screen their running exercises. We can tell whether the member is associated with running activity utilizing the accelerometer information, and keeping in mind the end goal to expel commotion, we generally set the base span of identifying a client's contribution in rushing to be 10 minutes [21]. Cell phone Usage (Calendar, Battery and Camera) Related Questions. The inquiries got from the schedule occasions is No. 3 "Is there a thing made arrangements for one week from now in your date-book?". As asked for by members, we just recorded whether there would be a thing arranged in next couple of days in the logbook; we didn't get to the substance of any arranged thing in the timetable as it is a serious attack of protection. We utilize the comparative arrangement to create genuine/false inquiries identified with battery charging and camera utilization utilizing Android API: "Did you accomplish something with battery/camera in the previous one or few days?" (Question No. 4 and 5 in Table 2). Inquiries on Legacy App Usage: Contact, Call, SMS. We create genuine/false inquiries identified with contact, call, SMS correspondingly. For instance, No. 7 question is: "Would someone say someone is in your contacts on the telephone?". Genuine/false inquiries can be created in light of call and SMS history utilizing the comparable arrangement: "Did you call/content somebody?". Like other genuine/false inquiries, the right response to this inquiry is haphazardly set as obvious or false with an equivalent likelihood. In the event that the right answer is set as "genuine", we haphazardly pick a name in the telephone's contact, and supplant "somebody" in the inquiry with this picked name truly. Generally if the right answer is set as "false", we make a phony name to supplant "somebody" in the inquiry by the approach proposed by Luo et all. This approach haphazardly picks a first name and a last name in telephone's contact list, without crashing into a current name in the rundown. Inquiries on Third-Party App Installment and Usage. We acquire a rundown of outsider applications by means of Android API, and we additionally screen the use of these applications. We sift through "launcher" applications and Event Log itself in our checking test. "Launcher" applications are the default home screen applications on Android, e.g., "Samsung Desktop". As the investigation [23] specifies, "launcher" applications are the most as often as possible called ones on Android frameworks, while clients may not know about their unexpected use of it. From that point onward, we can create a genuine/false inquiry like the heritage application: "Did you introduce/utilize some application on your telephone (in the previous few days)?". 4.2 Multiple-Choice And Blank-Filling Questions We make "W" inquiries as different decision and clear filling by essentially broadening the genuine/false inquiries on heritage and outsider applications. For instance, a genuine/false inquiries can be effortlessly stretched out to be a "W" question: "who did you call/content?" (approaching and active calls/SMS were dealt with similarly), or a recurrence based "W" question: "Which application did you utilize generally often?". Answers to Multiple-Choice Questions. For every multiplechoice question, there are four alternatives (just a single right choice). The right alternative is haphazardly picked with an equivalent likelihood of being any choices. For instance, with respect to Question No. 28 "Who did you call a week @IJAERD-2018, All rights Reserved


International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 ago?", we haphazardly pick a name in member's last week call records, and the rest three are faked by names in the contact (in the interim not showing up in the call records), at that point we arbitrarily rearrange these names to be the alternatives of the inquiry. We check the quantity of calls (or SMS) from/to each contact, or the circumstances an application is utilized by a member, for making the recurrence based inquiry, e.g., No. 34 "Who was your most regular contact a week ago?". On the off chance that there are in excess of one most incessant contacts or most much of the time utilized applications, any answer inside these applicants is viewed as right. Answers to Blank-Filling Questions. For each clear filling inquiry, we have a default adjust answer that is set by our framework, and in addition an answer contribution by the member in the memory test. We utilize the accompanying technique to decide if an information answer coordinates the default rectify one. To start with, we can without much of a stretch sift through worthless answers, and after that we obtain the approach proposed by Stuart Schechter et al [4] to look at the info and default answers, i.e., to evacuate all non-alphanumeric characters, drive letters into bring down cases, and permit one blunder (an enhanced variant of alter separate cost) for each five characters in the default reply. 5 EVALUATION AND EXPERIMENTS RESULTS 5.1 Experiment Setup The unwavering quality and security of our framework primarily depended on the mystery addresses that Secret-QA made, so we completed a client concentrate to assess the execution of our framework. Note that later on work, we will consider setting up a probabilistic model in light of a vast size of client information to portray the dependability and the security of the mystery questions. In our analyses, we selected 88 volunteer members, and completed a three-stage examination to ponder the security and unwavering quality of mystery addresses that were made utilizing cell phone sensor and application information. 5.1.1 Participant Recruitment An aggregate number of 88 understudies (48 guys versus 40 females) in a college were enlisted, barring the individuals from our exploration lab. Every member was first requested that a survey demonstrate their experience of utilizing OSNs, secret key recuperation techniques, and cell phones. Results demonstrate that numerous members with a noteworthy in Chinese Literature may have less understanding on cell phones' sensors; notwithstanding, all understudies whose major is software engineering know about the ideas above. Henceforth, in our examination, we utilize these gatherings of understudies as agents of different populaces for the accompanying two reasons: (1) The extent of this work is to think about in the case of utilizing cell phone sensor/application information is useful for mystery question based optional verification, and along these lines we have to avoid the effect of social and statistic factors however much as could reasonably be expected in the trial, and understudies are the populace that gets minimal friendship from these variables; (2) Young individuals like understudies have the essential experience on setting and noting mystery addresses (or finishing this trial), and they utilize cell phones and online apparatuses (e.g. OSNs, web crawlers) consistently. As needs be, members in our investigation meet the accompanying prerequisites. 1) Participants ought to be students or graduates , and ought not be full-time utilized (i.e., his/her occupation should at present be understudy); 2) Participants ought to have utilized Android1 cell phone for no less than one year; 3) Participants should access at any rate once every week to one of the notable OSNs (inside the rundown of long range informal communication sites gave by [24]). Necessity 1 is set as far as possible their social foundations, ages and vocations, on the grounds that these statistic components may bring about contrast of memory execution [25]. Prerequisites 2 and 3 guarantee that members have the sagacious on cell phones and OSNs to finish our trials. 5.1.2 Three-Phase Experiment Design We completed a three-stage analyze as represented in Fig. 2: we initially gathered members' sensors/applications information utilizing Event Log (however just 42 members consented to introduce Event Log because of security concerns), at that point we requested that members answer questions identified with their information; in the mean time the figure assaults were propelled by members' associates and outsiders; the confirmation succeeded if the member could give most right answers in many inquiries. At last, we welcomed members to give criticisms on tests. Level of Acquaintance Between Participants and EventLog Installation. A survey was appointed to members to demonstrate the colleague connection between any two of them. We just utilized whole numbers of 1, 2 and 3 to characterize three expanded levels of associate between two distinctive participants– "never caught wind of", "think about him/her", "colleague". We pick Android cell phones for tests on account of its open API to catch the sensor and application information, with no inclination on the cell phone working frameworks. ZHAO ET AL.: UNDERSTANDING SMARTPHONE SENSOR AND APP DATA FOR ENHANCING THE SECURITY OF SECRET QUESTIONS 557 was emphatically unwilling to reply (because of protection issues). It was likewise doable that a member did not keep our EventLog online constantly, and along these lines his information was inadequate to create every mystery question. Furthermore, every member ought to demonstrate the degree of intrusion of security for each inquiry, points of interest will Assignment of Acquaintance/Stranger @IJAERD-2018, All rights Reserved


International Journal of Advance Engineering and Research Development (IJAERD) Volume 5, Issue 03, March-2018, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Attackers. In the investigation, all members joined the speculating assaults against every member in Group A, to such an extent that every member in Group An eventual assaulted by no less than three associates (that had shown a level of colleague estimation of 3) and no less than three outsiders (that had demonstrated a level of colleague estimation of 1). Note that a member in Group A could be a member who joined the memory test, and furthermore an aggressor who speculated the solutions to others' inquiries. Our lab aides appropriated each assailant a piece of paper, demonstrating their assault focuses with understudy IDs and full names. Targets were a mix of colleagues and outsiders, up to four individuals. In each assault propelled by the aggressor I against member j 2 A, the assailant would secure similar inquiries their objective had replied. They were first required to figure the responses to inquiries of every one of their objectives, with no outside help. At that point, we urged them to utilize web crawlers, OSNs and grounds data frameworks (open online instruments) to research and figure the appropriate response once more. Counteractive action of Cheating in Phase 2. To wipe out insights and anticipate plot however much as could be expected amid the tests, we authorized the accompanying tenets in Phase 2. Each inquiry could be addressed once and just once by means of our custom constructed web-survey interface. Members would not know the following inquiry before completing the present one. In the event that an inquiry would be asked in excess of one sort, at that point this inquiry would show up in the request of "clear filling", "various decision" and after that "genuine/false". We confined members from speaking with each other by requesting that they kill their cell phones (reported as an affability to different members), segregating them in discrete rooms, and observing their practices. All members were delegated by lab associates who were in charge of forestalling bamboozling, checking their online devices use, and giving members direction about how to include on the web-survey also. CONCLUSION In this paper, we present a Secret-Question based Authentication system, called “Secret-QA”, and conduct a user study to understand how much the personal data collected by smartphone sensors and apps can help improve the security of secret questions without violating the users’ privacy. We create a set of questions based on the data related to sensors and apps, which reflect the users’ short-term activities and smartphone usage. We measure the reliability of these questions by asking participants to answer these question, as well as launching the acquaintance/stranger guessing attacks with and without help of online tools, and we are considering establishing a probabilistic model based on a large scale of user data to characterize the security of the secret questions. In our experiment, the secret questions related to motion sensors, calendar, app installment, and part of legacy apps (call) have the best performance in terms of memorability and the attack resilience, which outperform the conventional secret-question based approaches that are created based on a user’s long-term history/information. REFERENCES [1] R. Reeder and S. Schechter, “When the password doesn’t work: Secondary authentication for websites,” IEEE Security Privacy., vol. 9, no. 2, pp. 43–49, Mar. 2011. [2] M. Zviran and W. J. Haga, “User authentication by cognitive passwords: An empirical assessment,” in Proc. 5th Jerusalem Conf. Inf. Tech., Next Decade Inf. Tech., (Cat. No. 90TH0326-9), 1990, pp. 137–144. [3] J. Podd, J. Bunnell, and R. Henderson, “Cost-effective computer security: Cognitive and associative passwords,” in Proc., 6th Australian Conf. Comput.-Human Interaction, 1996, pp. 304–305. [4] S. Schechter, A. B. Brush, and S. Egelman, “It’s no secret. measuring the security and reliability of authentication via secret questions,” in Proc. 30th IEEE Symp. Security Privacy., 2009, pp. 375–390. [5] S. Schechter, C. Herley, and M. Mitzenmacher, “Popularity is everything: A new approach to protecting passwords from statistical-guessing attacks,” in Proc. 5th USENIX Conf. Hot Topics Security, 2010, pp. 1–8. [6] D. A. Mike Just, “Personal choice and challenge questions: A security and usability assessment,” in Proc. 5th Symp. Usable Privacy Security, p. 8. ACM, 2009. [7] A. Rabkin, “Personal knowledge questions for fallback authentication: Security questions in the era of facebook,” in Proc. 4th Symp. Usable Privacy Security, 2008, pp. 13–23. [8] J. C. Read and B. Cassidy, “Designing textual password systems for children,” in Proc. 11th Int. Conf. Interaction Des. Children, 2012, pp. 200–203. [9] H. Ebbinghaus, Memory: A Contribution to Experimental Psychology. New York, NY, USA: Teachers college, Columbia University, 1913, no. 3. @IJAERD-2018, All rights Reserved


Privacy and secure authentication using cooperative query answer ijaerdv05i0312606  
Privacy and secure authentication using cooperative query answer ijaerdv05i0312606