ISSN ONLINE(2320-9801) PRINT (2320-9798)

Yakışıklı erkek tatil için bir beldeye gidiyor burada kendisine türk Porno güzel bir seksi kadın ayarlıyor Onunla beraber otel odasına gidiyorlar Otel odasına rokettube giren kadın ilk önce erkekle sohbet ederek işi yavaş halletmeye çalışıyor sex hikayeleri Kocası fabrikatör olan sarışın Rus hatun şehirden biraz uzak olan bir türk porno kasabaya son derece lüks bir villa yaptırıp yerleşiyor Kocasını işe gönderip mobil porno istediği erkeği eve atan Rus hatun son olarak fotoğraf çekimi yapmak üzere türk porno evine gelen genç adamı bahçede azdırıyor Güzel hatun zengin bir iş adamının porno indir dostu olmayı kabul ediyor Adamın kendisine aldığı yazlık evde sikiş kalmaya başlayan hatun bir süre sonra kendi erkek arkadaşlarını bir bir çağırarak onlarla porno izle yapıyor Son olarak çağırdığı arkadaşını kapıda üzerinde beyaz gömleğin açık sikiş düğmelerinden fışkıran dik memeleri ile karşılayıp içeri girer girmez sikiş dudaklarına yapışarak sevişiyor Evin her köşesine yayılan inleme seslerinin eşliğinde yorgun düşerek orgazm oluyor

A Structure of Adaptive Portable Video Streaming and Efficient Common Video Cassette Sharing In the Clouds

M.Mahipal1 and M.Bhaskar2
  1. Student, Dept of Software Engineering, Vidyabharathi Institute of Technology, Warangal, Telangana, India
  2. HOD, Assistant Professor, Dept of Computer Science & Engineering, Vidyabharathi Institute of Technology, Warangal, Telangana, India
Related article at Pubmed, Scholar Google

Visit for more related articles at International Journal of Innovative Research in Computer and Communication Engineering

Abstract

Utilizing the reasoning processing technology, a new cellular movie loading structure, known as AMESCloud, which has two main parts: AMoV (adaptive cellular movie streaming) and ESoV (efficient public movie sharing). AMoV and ESoV build a personal broker to offer movie loading services efficiently for each cellular customer. For a given customer, AMoV lets her undercover broker adaptively adjust her stream flow with a scalable movie programming technique in accordance with the reviews of link quality. Likewise, ESoV watches the online community communications among cellular users, and their personal providers try to prefetch movie content in advance. We apply a model of the AMES-Cloud structure to show its performance. It is shown that the personal providers in the atmosphere can effectively offer the flexible loading, and perform movie discussing (i.e., prefetching) in accordance with the online community analysis.. We first recommend an precise Comparison-based Profile Related method (eCPM) which runs between two parties, an initiator and a -responder. The eCPM enable the initiator to acquire the comparison-based matching outcome about a specified feature in their profiles, while stop their feature values from exposure. We then recommend an implied Comparison-based Profile Related method (iCPM) which allows the initiator to straight acquire some information instead of the evaluation outcome from the -responder. The information unique to customer profile can be separated into multiple groups by the -responder. The initiator unquestioningly selects the involved category which is unknown to the -responder.

 

Keywords

AMES,AMOV,ESOV,ECPM,ICPM

INTRODUCTION

While receiving movie loading traffic via 3G/4G cellular networks, cellular customers often suffer from long shield efforts and sporadic interruptions due to the limited data transfer useage and weblink situation fluctuation caused by multi-path adding and customer flexibility Thus, it is crucial to enhance the support high high quality of cellular movie loading while using the networking and estimate resources efficiently. Lately there have been many studies on how to enhance the support high high quality of cellular movie loading on two aspects:Scalability: Mobile movie loading military should assistance a wide variety of cellular devices; they have different movie solutions, different processing abilities, different wi-fi hyperlinks (like 3G and LTE) and so on. Also, the available weblink capacity of a cellular phone may vary eventually and area based on its signal strength, other customers traffic in the same cell, and weblink situation difference. Storing several versions (with different bit rates) of the same movie material may have high expense in terms of storage area and communication. To deal with this problem, the Scalable Video Programming (SVC) technique (Annex G extension) of the H.264 AVC movie pressure standard defines a first layer (BL) with several enhance layers (ELs). These sub sources can be secured by taking advantage of three scalability features: (i) spatial scalability by adding picture (screen pixels), (ii) temporary scalability by adding the frame amount, and (iii) high quality scalability by adding the picture pressure. By the SVC, video clips clip can be decoded/played at the lowest high quality if only the BL is provided. However, the more ELs can be provided, the better class of it clip flow is achieved.

Adaptability

Traditional movie loading methods designed by taking into consideration relatively constant traffic hyperlinks between servers and customers, perform badly in cellular surroundings [2]. Thus the fluctuating wi-fi weblink position should be properly dealt with to provide „tolerable” movie loading solutions. To deal with this problem, we have to adjust it clip bit amount adjust to the currently time-varying available weblink data transfer useage of each cellular customer. Such flexible loading methods can successfully reduce bundle failures and data transfer useage waste. Scalable movie coding and flexible flow methods can be together combined to accomplish successfully the best possible high high quality of movie loading solutions. Thus the problem is that the server should take over the substantial processing expense, as the number of customers increases. have suggested to make customized brilliant providers for maintenance cellular customers, e.g., Cloudlet and Status. This is because, in the reasoning, many broker instances (or threads) can be maintained dynamically and efficiently based on the time-varying customer demands. Lately online community solutions (SNSs) have been popular. There have been suggestions to enhance the high high quality of material delivery using SNSs [23] [24]. In SNSs, customers may share, comment or re-post video clips among buddies and associates in the same team, which implies a customer may observe video clips clip that her buddies have recommended (e.g. [24]). Users in SNSs can also follow famous and accepted customers based on their interests (e.g., an official twitter or facebook account that shares the latest pop music videos), which is likely to be watched by its followers. In this regard, we are further inspired to manipulate the relationship among cellular customers from their SNS behavior to be able to prefetch in advance the beginning part of it clip or even the whole movie to the associates of a team who have not seen it clip yet. It can be done by a background job assistance by the broker (of a member) in the cloud; once the customer mouse clicks to discover the shocking truth, it can instantly begin to play. In this document, we design a flexible movie loading and prefetching structure for cellular customers with the above objectives in thoughts, known as AMES-Cloud. AMES-Cloud constructs a personal broker for each cellular customer in reasoning processing surroundings, which is used by its two main parts: (i) AMoV (adaptive cellular movie streaming), and ESoV (efficient public movie sharing). The efforts of this document can be described as follows:AMoV offers the best possible loading experiences by adaptively controlling the loading bit amount depending on the fluctuation of the weblink high quality. AMoV adapts the bit amount for each customer utilizing the scalable movie coding. The personal broker of a customer keeps track of the reviews to be able on the weblink position. Private providers of customers are dynamically started and enhanced in the reasoning estimate platform. Also the realtime SVC coding is done on the reasoning processing side efficiently.

AMES-CLOUD FRAMEWORK

In this section we explain the AMES-Cloud framework includes the Adaptive Mobile Video stream (AMoV) and the Efficient Social Video sharing (ESoV). As shown in Fig. 1, the whole video storing and streaming system in the cloud is called the Video Cloud (VC). In the VC, there is a large-scale video base (VB), which stores the most of the popular video clips for the video service providers (VSPs). A sequential video base (tempVB) is used to cache new candidates for the popular videos, while tempVB counts the access frequency of each video. The VC keeps running a collector to seek videos which are by now popular in VSPs, and will re-encode the collected videos into SVC format and store into tempVB first. By this 2-tier storage, the AMES-Cloud can keep serving most of popular videos eternally. Note that management work will be handled by the manager in the VC. Specialized for each mobile user, a sub-video cloud (subVC) is created dynamically if there is any video streaming demand from the user. The sub-VC has a sub video base (subVB), which stores the recently fetched video segments. Note that the video deliveries among the subVCs and the VC in most cases are actually not “copy”, but just “link” operations on the same file eternally within the cloud data center [36]. There is also encoding function in subVC (actually a smaller-scale encoder instance of the encoder in VC), and if the mobile user demands a new video, which is not in the subVB or the VB in VC, the subVC will fetch, encode and transfer the video. During video streaming, mobile users will always report link conditions to their corresponding subVCs, and then the subVCs offer adaptive video streams. Note that each mobile device also has a provisional caching storage, which is called local video base (localVB), and is used for buffering and prefetching.

A. Social Content Sharing

In SNSs, users subscribe to known friends, celebrities, and particular fascinated material marketers as well; also there are various types of community actions among customers in SNSs, such as immediate concept and community publishing. For growing video clips in SNSs, one can publish video clips clip in the community, and his/her members can easily see it; one can also straight suggest video clips clip to specified friend(s); furthermore one can regularly get observed by signed up material founder for new or well-known video clips. Just like research in [23] [24], we define different durability levels for those community actions to indicate the possibility that it clip distributed by one customer may be viewed by the devices of the one?s discussing actions, which is known as a “hitting probability”, so that subVCs can bring out effective qualifications prefetching at subVB and even localVB. Because after video clips clip discussing action, there may be a certain wait that the receiver gets to know the discussing, and triggers to look at [38]. Therefore the prefetching in prior will not effect the customers at most situations. Instead, a customer can just click to see without any streaming wait as the starting part or even the whole movie is already prefetched at the localVB. The amount of prefetched sections is mainly identified by the durability of the community actions. And the prefetching from VC to subVC only represents the “linking” action, so there is only file finding and connecting functions with small delays; the prefetching from subVC to localVB also relies on the durability of the community actions, but will also think the wi-fi weblink position.We categorize the community actions in present well-known SNSs into three types, regarding the effect of the actions and the prospective responding concern from the perspective of the recipient:
_ Subscription: Like the popular RSS services, an user can subscribe to a particular video publisher or a special video collection tune based on his/her interests. This interest-driven connectivity between the subscriber and the video publisher is considered as “median”, because the subscriber may not always watch all subscribed videos.
_ Direct recommendation: In SNSs, an user directly recommend a video to particular friend(s) with a short message. The recipient of the message may watch it with very high probability. This is considered as “strong”.
_ Public sharing: Each user in SNSs has a timeline-based of activity stream, which shows his/her recent activities. The activity of a user inspection or sharing a video can be seen by his/her friends (or followers). We consider this public distribution with the “weak” connectivity among users, because not many people may watch the video that one has seen without direct recommendation.

B. Prefetching Levels

Different strengths of the social activities point to different levels of probability that a video will be soon watched by the recipient. Correspondingly we also define three prefetching levels regarding the social activities of mobile users:
_ “Parts”: Because the videos that published by subscriptions may be watched by the subscribers with a not high probability, we propose to only push a part of BL and ELs segment, for example, the first 10% segments.
_ “All”: The video shared by the direct recommendations will be watched with a high probability, so we propose to prefetch the BL and all ELs, in order to let the recipient(s) directly watch the video with a good quality, without any buffering.
_ “Little”: The public sharing has a weak connectivity among users, so the probability that a user?s friends (followers) watch the video that the user has watch or shared is low. We propose to only prefetch the BL segment of the first time window in the opening to those who have seen his/her activity in the stream.The prefetching happens among subVBs and the VB, also more importantly, will be performed from the subVB to localVB of the mobile device depending on the link quality. If a mobile user is covered by Wi-Fi access, due to Wi-Fi?s capable link and low price (or mostly for free), subVC can force as much as possible in most situations. However if it is with a 3G/4G relationship, which expenses a lot and experiences restricted data transfer useage, we recommend to restrict the prefetching stage to preserve power and price as detailed in Desk. 1, but customers can still benefit from the prefetching successfully. Observe that some power forecast technique can be implemented to be able to definitely choose whether present battery power position is appropriate for “parts” or “little” [39]. If a customer, A, gets the immediate suggestions of video clips clip from another customer, B, A?s subVC will instantly prefetch it clip either from B?s subVB, or from the VB at the stage of all if A is with Wi-Fi accessibility. However if customer A is linked with 3G/4G weblink, we will precisely prefetch a aspect of it clip department to A?s regional storage space at the stage of “parts”. Observe that the signed up video clips will be not prefetched when customer A is at 3G/4G relationship, as it is reduced from “little” tonone. A better expansion of the prefetching technique by public actions can be developed by an self-updating device from the user?s reaching record in an transformative way. This learning-based prefetching is out of the opportunity of this document, and will be researched as our upcoming perform.

PROPOSED MODEL

EXPLICIT COMPARISON-BASED APPROACH

In this section, we present the explicit Comparison-based Profile Matching protocol, i.e., eCPM. This protocol allows two users to compare their attribute values on a specified attribute without disclose the values to each other. But, the protocol reveals the comparison result to the initiator, and therefore offers qualified anonymity.

a. Bootstrapping

The protocol has a fundamental bootstrapping phase, where the TCA generate all system parameters, user pseudonyms, and keying materials. Specifically, the TCA runs G to generate?p, q, R, R, χ? for initiating the homomorphic encryption (see Sec. III-A). The TCA generates a pair of public and private keys for itself. The public key image is open to all users; the private key image is a secret which will be used to issue certificates for user pseudonyms and keying materials, as shown below. In this section, we propose the implicit Comparison-based Profile Matching (iCPM) by adopting the oblivious transfer cryptographic technique [40]. We consider users have distinct values for any given attribute. As shown in Fig. 3, the iCPM consists of three main steps. In the first step, imagechooses an involved category image by setting y-th element to 1 and other elements to 0 in a λ- length vector image image then encrypt the vector by using the homomorphic encryption and sends the encrypted vector to image Thus, imageis unable to know T but still can process on the cipher text. In the second step, image computes the ciphertexts with input of self-defined messages image for image two encrypted vectors image and its own attribute value image In the last step, u in the last step imagedecrypts the cipher text and obtain imageif image or image if image
a.Protocol Steps
image
image
The confidentiality of customer profiles is certain by the homomorphic security. The evaluation outcome is always in the secured structure, and is not straight revealed to The exposed details is either or which is irrelevant to customer profiles. Therefore, the method get in touch with do not help in wondering the profiles, and the complete privacy is offered. Meanwhile, vector is always in an secured structure so that is incapable to know the fascinated type of . Moreover, guarantees that only one of and will be exposed to The non-forgeability residence is just like that of the eCPM. will not lie as it creates trademark ) and gives it to . The profile bogus strike will be recognized if reviews the trademark to the TCA. Moreover, has no need to lie as it can accomplish the same purpose by basically change the material of and.

IMPLICIT PREDICATE-BASED APPROACH

Both the eCPM and the iCPM perform profile matching on a single attribute. For a matching connecting multiple attributes, they have to be executed multiple times, each time on one attribute. In this section, we extend the iCPM to the multi attribute cases, without jeopardizing its anonymity property, and obtain an implicit Predicate-based Profile Matching protocol, i.e., iPPM. This protocol relies on a predicate which is a logical expression made of multiple comparisons across distinct attributes and thus supports complicated matching criteria within a single protocol run. As shown in Fig. the iPPM is composed of three main steps. In the first step, dissimilar from the iCPM, image n encrypted vectors of its attribute values corresponding to the attributes in A where A (|A| = n = w) is the attribute set of the predicate _. In the second step, image sets 2λ polynomial functions image for image generates 2λn secret shares from image by chossing image and arranges them in a certain structure according to the predicate For every 2λ secret shares with the same index h, similar to the step 2 of the iCPM, image generates θ ciphertexts. image sends to image obtains nθ ciphertexts at the end of the second step. In the third step, image decrypts these nθ ciphertexts and finds n secret shares of finally can obtain image or image from the secret shares.

a.Protocol Steps

The iPPM is obtained by combining the iCPM with a secret sharing scheme to support a predicate matching. The initiator image sends its attribute values matching to the attributes in A to the responder image Without loss of generality, we assume image Then, image defines a predicate image where the comparison operator opt is either > or < and image The predicate contains n number of requirements (i.e., comparisons), each for a distinct a The responder image determine λ pairs of messages image receives image for attributes image The initiator image if at least image f the n requirements are satisfied, otherwise. Similar to the iCPM, image but unknown to image is determined by image The threshold gate image is chosen by When n = 1, the iPPM reduces to the iCPM. The protocol steps are given below.
image
image

b. Effectiveness Discussion

The correctness of the iPPM is as follows. At Step 2, the responder image executes the Step 2 of the iCPM n times, each time it effectively delivers only one secret share of either imageor image or image When image receives either image shares of image or image shares of image it can recover either image or image. The interpolation function corresponding to the secret sharing scheme always guarantees the correctness. The anonymity and non-forgeability of the iPPM are achieved similar to those of the iCPM and the eCPM, respectively.

PERFORMANCE EVALUATION

The eCPM+ details accumulative privacy threat in several method operates and music itself instantly to maintain preferred privacy durability. Some past works are involved only with the privacy threat introduced by each individual method run, and some performs reduce privacy threat by personally modifying certain limit principles. Though they provide the depending privacy as the eCPM, they are not much like the eCPM and the eCPM+ because the privacy protection of customers is considered with regards to successive method operates. Therefore, in this area we assess the eCPM+ (which uses a pre-adaptive pseudonym modify strategy) in evaluation with two other eCPM versions, respectively utilizing a continuous pseudonym modify period (CONST-z) and a post-adaptive pseudonym modify technique (Post).

Simulation Setup

Our simulator research is in accordance with the real track [48] gathered from 78 customers participating a meeting during a four-day interval. A get in touch with indicates that two customers come near to each other and their connected Wireless gadgets identify each other. The users? Wireless gadgets run a finding system every 120 a few moments on regular and signed about 128, 979 connections. Each get in touch with is recognized by two customers, a start-time, and a length. In CONST-z, we set the pseudonym modify interval z from 1 to 40 (time slots); in the postadaptive and pre-adaptive techniques, we set pseudonym life-time aspect ξ = 30. In the pre-adaptive strategy, we use ARMA order (10, 5).
Anonymity break period under the constant strategy anonymity break period experienced by each user with the constant strategy being used. It can be seen that when z = 1, each user experiences the shortest anonymity break period at the cost of 10, 000 pseudonyms per user. Anonymity break is still possible in this extreme case because users may have multiple contacts within a single time slot while they are still using the same pseudonym. If a user has a more restrictive anonymity requirement (e.g., from 10-anonymity to 30-anonymity) or uses a larger pseudonym change interval (from 1 time slot to 20 time-slots), it will have more corrupted pseudonyms and thus suffer a longer period of anonymity break.

Anonymity risk level over time (th = 0.15)

We choose the 32nd customer, who in common has lower privacy threat stage than the 7th customer, and show its 10- anonymity threat stage in two successive time times (2000, 3200) and (8200, 9400) with the post-adaptive technique in Fig. The privacy threat stage limit is th = 0.15. In the figure, the fall from a risky stage to a low threat stage indicates Remember that a customer changes its pseudonym not only when the privacy threat stage is beyond limit th but also when its current pseudonym ends. This is reflected by the privacy threat stage fall occurred below the limit line in the figure. From Fig we can see that the pseudonym change regularity is great when the customer activities a huge variety of others who live nearby. This is affordable as a huge variety of profile related operates are implemented in this case, and the user?s privacy threat stage develops quickly. When the stage is beyond a pre-defined limit, the customer changes its pseudonym.

CONCLUSION

We have examined a exclusive comparison-based profile related issue in Cellular Public Systems (MSNs), and suggested novel methods to fix it. The precise Comparison based Profile Matching (eCPM) method provides depending privacy. It shows the evaluation outcome to the initiator. Consider the k-anonymity as a customer need, we evaluate the privacy threat stage in regards to the pseudonym change for successive eCPM operates. We have further presented an improved edition of the eCPM, i.e., eCPM+, by taking advantage of the prediction-based technique and implementing the pre-adaptive pseudonym modify. The potency of the eCPM+ is verified through comprehensive models using realtrace details. We have also developed two methods with complete privacy, i.e., implied Comparison-based Profile Matching (iCPM) and implied Predicate-based Profile Matching (iPPM). The iCPM manages profile related depending on only one evaluation of an feature while the iPPM is applied with a sensible appearance made of several evaluations comprising several features. The iCPM and the iPPM both allow customers to anonymously demand for details and react to the demands according to the profile related outcome, without exposing any profile details.

Figures at a glance

Figure 1 Figure 2 Figure 3 Figure 4
Figure 1 Figure 2 Figure 3 Figure 4
 

References


  1. A. G. Miklas, K. K. Gollu, K. K. W. Chan, S. Saroiu, P. K. Gummadi, and E. de Lara, “Exploiting social interactions in mobile systems,” inUbicomp, 2007, pp. 409–428.

  2. S. Ioannidis, A. Chaintreau, and L. Massoulie, “Optimal and scalable distribution of content updates over a mobile social network,” in Proc. IEEEINFOCOM, 2009, pp. 1422–1430.

  3. R. Lu, X. Lin, and X. Shen, “Spring: A social-based privacy-preserving packet forwarding protocol for vehicular delay tolerant networks,” inProc. IEEE INFOCOM, 2010, pp. 632–640.

  4. W. He, Y. Huang, K. Nahrstedt, and B. Wu, “Message propagation in adhoc-basedproximity mobile social networks,” in PERCOM workshops, 2010, pp. 141–146.

  5. D. Niyato, P. Wang, W. Saad, and A. Hjørungnes, “Controlled coalitional games for cooperative mobile social networks,” IEEE Transactions onVehicular Technology, vol. 60, no. 4, pp. 1812–1824, 2011.

  6. M. Motani, V. Srinivasan, and P. Nuggehalli, “Peoplenet: engineering a wireless virtual social network,” in MobiCom, 2005, pp. 243–257.

  7. M. Brereton, P. Roe, M. Foth, J. M. Bunker, and L. Buys, “Designing participation in agile ridesharing with mobile social software,” in OZCHI,2009, pp. 257–260.

  8. E.Bulut and B.Szymanski, “Exploiting friendship relations for efficient routing in delay tolerant mobile social networks,” IEEE Transactions onParallel and Distributed Systems, vol. 23, no. 12, pp. 2254–2265, 2012.

  9. Z. Yang, B. Zhang, J. Dai, A. C. Champion, D. Xuan, and D. Li, “E-smalltalker: A distributed mobile system for social networking in physicalproximity,” in ICDCS, 2010, pp. 468–477.

  10. Q. Li, S. Zhu, and G. Cao, “Routing in socially selfish delay tolerantnetworks,” in Proc. IEEE INFOCOM, 2010, pp. 857–865.

  11. X. Liang, X. Li, T. H. Luan, R. Lu, X. Lin, and X. Shen, “Moralitydriven data forwarding with privacy preservation in mobile social networks,”IEEE Transactions on Vehicular Technology, vol. 7, no. 61, pp. 3209–3222, 2012.

  12. R. Gross, A. Acquisti, and H. J. H. III, “Information revelation and privacy in online social networks,” in WPES, 2005, pp. 71–80.

  13. F. Stutzman, “An evaluation of identity-sharing behavior in social network communities.” iDMAa Journal, vol. 3, no. 1, pp. 10–18, 2006.

  14. K. P. N. Puttaswamy, A. Sala, and B. Y. Zhao, “Starclique: guaranteeing user privacy in social networks against intersection attacks,” inCoNEXT, 2009, pp. 157–168.

  15. E. Zheleva and L. Getoor, “To join or not to join: the illusion of privacy in social networks with mixed public and priv ate user profiles,” inWWW, 2009, pp. 531–540.

  16. G. Chen and F. Rahman, “Analyzing privacy designs of mobile social networking applications,” IEEE/IFIP International Conference onEmbedded and Ubiquitous Computing, vol. 2, pp. 83–88, 2008.

  17. D. Balfanz, G. Durfee, N. Shankar, D. K. Smetters, J. Staddon, and H.C. Wong, “Secret handshakes from pairing-based key agreements,” inIEEE Symposium on Security and Privacy, 2003, pp. 180–196.

  18. M. J. Freedman, K. Nissim, and B. Pinkas, “Efficient private matching and set intersection,” in EUROCRYPT, 2004, pp. 1–19.

  19. R. Lu, X. Lin, X. Liang, and X. Shen, “A secure handshake scheme with symptoms-matching for mhealthcare social network,” ACM MobileNetworks and Applications (MONET), vol. 16, no. 6, pp. 683–694, 2011.

  20. M. Li, N. Cao, S. Yu, and W. Lou, “Findu: Privacy-preserving personal profile matching in mobile social networks,” in Proc. IEEE INFOCOM,2011, pp. 2435–2443.

  21. R. Zhang, Y. Zhang, J. Sun, and G. Yan, “Fine-grained private matching for proximity-based mobile social networking,” in Proc. IEEEINFOCOM, 2012, pp. 1969–1977.

  22. W. Dong, V. Dave, L. Qiu, and Y. Zhang, “Secure friend discovery inmobile social networks,” in Proc. IEEE INFOCOM, 2011, pp. 1647–1655.

  23. J. Freudiger, M. H. Manshaei, J.-P. Hubaux, and D. C. Parkes, “On noncooperative location privacy: a game-theoretic analysis,” in ACM CCS,2009, pp. 324–337.

  24. R. Lu, X. Lin, H. Luan, X. Liang, and X. Shen, “Pseudonym changing at social spots: An effective strategy for location privacy in vanets,” IEEETransactions on Vehicular Technology, vol. 61, no. 1, pp. 86 – 96, 2011.

  25. J. Katz, A. Sahai, and B. Waters, “Predicate encryption supporting disjunctions, polynomial equations, and inner product s,” in EUROCRYPT,2008, pp. 146–162.

  26. N. Eagle and A. Pentland, “Social serendipity: mobilizing social software,” IEEE Pervasive Computing, vol. 4, no. 2, pp. 28–34, 2005.

  27. J. Teng, B. Zhang, X. Li, X. Bai, and D. Xuan, “E-shadow: Lubricating social interaction using mobile phones,” in ICDCS, 2011, pp. 909–918.

  28. B. Han and A. Srinivasan, “Your friends have more friends than you do: identifying influential mobile users through random walks,” inMobiHoc, 2012, pp. 5–14.

  29. Z. Yang, B. Zhang, J. Dai, A. C. Champion, D. Xuan, and D. Li, “E-smalltalker: A distributed mobile system for social networking in physicalproximity,” in ICDCS, 2010, pp. 468–477.

  30. L. Kissner and D. X. Song, “Privacy-preserving set operations,” in CRYPTO, 2005, pp. 241–257.

  31. Q. Ye, H. Wang, and J. Pieprzyk, “Distributed private matching and set operations,” in ISPEC, 2008, pp. 347–360.

  32. D. Dachman-Soled, T. Malkin, M. Raykova, and M. Yung, “Efficient robust private set intersection,” in ACNS, 2009, pp. 125–142.

  33. S. Jarecki and X. Liu, “Efficient oblivious pseudorandom function with applications to adaptive ot and secure computation of set intersection,”in TCC, 2009, pp. 577–594.

  34. C. Hazay and Y. Lindell, “Efficient protocols for set intersection and pattern matching with security against malicious and covert adversaries,”Journal of Cryptology, vol. 23, no. 3, pp. 422–456, 2010.

  35. B. Goethals, S. Laur, H. Lipmaa, and T. Mielikainen, “On private scalar product computation for privacy-preserving data mining,” in ICISC,2004, pp. 104–120. 2004, pp. 104–120.

  36. A. C.-C. Yao, “Protocols for secure computations (extended abstract),” in FOCS, 1982, pp. 160–164.

  37. O. Goldreich, S. Micali, and A. Wigderson, “How to play any mental game or a completeness theorem for protocols with honest majority,” inSTOC, 1987, pp. 218–229.

  38. Ioannidis, A. Grama, and M. J. Atallah, “A secure protocol for computing dot-products in clustered and distributed environments,” in ICPP, 2002,pp. 379–384.

  39. I. F. Blake and V. Kolesnikov, “Strong conditional oblivious transfer and computing on intervals,” in ASIACRYPT, 2004, pp. 515–529.

  40. P. Paillier, “Public-key cryptosystems based on composite degree residuosity classes,” in EUROCRYPT, 1999, pp. 223–238.

  41. M. Naehrig, K. Lauter, and V. Vaikuntanathan, “Can homomorphic encryption be practical?” in CCSW, 2011, pp. 113–124.

  42. R. Lu, X. Liang, X. Li, X. Lin, and X. Shen, “Eppa: An efficient and privacy-preserving aggregation scheme for secure smart gridcommunications,” IEEE Transactions on Parallel and Distributed Systems, vol. 23, no. 9, pp. 1621–1631, 2012.

  43. H. Ltkepohl, New introduction to multiple time series analysis. Springer, 2005.

  44. X. Liang, X. Li, Q. Shen, R. Lu, X. Lin, X. Shen, and W. Zhuang, “Exploiting prediction to enable secure and reliable routing in wireless bodyarea networks,” in Proc. IEEE INFOCOM, 2012, pp. 388–396.

  45. A. Shamir, “How to share a secret,” Communications of the ACM, vol. 22, no. 11, pp. 612–613, 1979.

  46. L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems,vol. 10, no. 5, pp. 557–570, 2002.

  47. J. Scott, R. Gass, J. Crowcroft, P. Hui, C. Diot, and A. Chaintreau, “CRAWDAD trace cambridge/haggle/imote/infocom (v. 2006-01-31),” Jan.2006.

  48. D. J. Watts, “Small worlds: The dynamics of networks between order and randomness,” J. Artificial Societies and Social Simulation, vol. 6, no.2, 2003.

  49. C. Bron and J. Kerbosch, “Finding all cliques of an undirected graph (algorithm 457),” Communications of the ACM, vol. 16, no. 9, pp. 575–576, 1973.