KR101928822B1 - System and method for computing a user's trust value of unknown device in IoT - Google Patents

Abstract

The present invention relates to a system and a method for calculating user′s reliability to an unknown device in an IoT environment. The method includes the steps of: selecting friends, who have high subjective experience similarity, as I-sharing friends by comparing an interaction history between a user and friends on a contextual basis; calculating user′s subjective reliability ((t^s)_(u, z)) for the target device by collecting and analyzing the subjective experience similarity (sim(p_u, p_x)) between the user and the I-sharing friends, and the reliability ((t^d)_(x, z)) of the I-sharing friends for the target device; and finally calculating the user′s reliability (T_(x, z)) for the target device by collecting and analyzing the user′s subjective reliability ((t^s)_(u, z)) for the target device, and a standardized reputation (a_(x, z)) of the I-sharing friends for the target device.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a system and a method for calculating a user's trust in an unfamiliar device in an Internet environment,

The present invention relates to a system and method for calculating user's trust in an unfamiliar device in an Internet environment of objects, and more particularly, to a system and method for calculating a user's trust in an unfamiliar device To a user reliability calculation system and method.

In recent years, advances in IoT have allowed users to use intelligent services through everyday objects in their immediate surroundings. When using these services, users often have to provide personal information (e.g., user location, status, behavior).

Conventionally, in order to prevent personal information from being misused by malicious objects, the interaction parties mainly use a shared security protocol. However, the main purpose of using a shared protocol is to protect the exchange information after deciding to interact with the other party, which does not help prevent the potential risk before the interaction.

There have been several studies to assess the reliability of interactions with untrusted objects prior to the interaction.

Nitti proposes a trust management method based on objective and subjective models (eg, co-location, collaborative work, parents, etc.) using social relationships between target nodes. To calculate the reliability of the target device, the objective model represents overall reputation, and the subjective model utilizes feedback from devices that share friendships with the target device.

Chen suggests a trust management approach that uses the sociability of the device owner. When referring to the indirect credibility of other device owners (eg, the reputation of the target device), the value of a more similar owner in terms of friendship, social contact, and interest communities is referred to as a higher weight. This sociability-based approach assumes that the overall sociality of the participant node is known to everyone in advance. However, this assumption can not be achieved in practice, especially for public smart devices that users often encounter without previous interaction experiences.

In order to solve this problem, Son proposed to predict the reliability of the target device based on the user interaction history with the device of the same type (that is, indirect interaction) with the target device in the current situation, There is a problem in that the accuracy of the reliability estimation is lowered.

Therefore, there is a need for a method that reliably complements the direct experience of public smart objects and can estimate the reliability more accurately.

M. Nitti, R. Girau, and L. Atzori, "Trustworthiness Management in the Social Internet of Things," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 5, pp. 1253-1266, 2014. I.R. Chen, J. Guo, and F. Bao, " Trust management for so-based iot and its application to service composition, " IEEE Transactions on Services Computing 9.3 (2016): 482-495. H. Son, N. Kang, B. Gwak, and D. Lee, "An Adaptive iot Trust Estimation Scheme Combining Interaction History and Stereotypical Reputation," in Proceedings of the IEEE Annual Consumer Communi- cations and Networking Conference (CCNC). IEEE, 2017.

In order to solve the above problems, the present invention provides a system and a method for calculating a user's trust in an unfamiliar device in an object-internet environment, in which user's trustworthiness of a strange device can be calculated more accurately by utilizing subjective experience of friends I want to.

The objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

)를 수집 및 분석하여 상기 대상 기기에 대한 사용자의 주관적 신뢰도(

)를 계산하는 단계; 및 상기 대상 기기에 대한 사용자의 주관적 신뢰도(

), 및 상기 대상 기기에 대한 I-sharing 친구들의 정형화된 표준 평판(a_x,z)을 수집 및 분석하여 상기 대상 기기에 대한 사용자의 신뢰도(T_x,z)를 최종 계산하는 단계를 포함하는 사물 인터넷 환경에서 낯선 기기에 대한 사용자 신뢰도 계산 방법을 제공한다.According to an aspect of the present invention, there is provided a method for managing an I-sharing friend, the method comprising: comparing I-sharing history of a user with friends on a situation basis; The subjective experience similarity (sim (p _u , p _x )) between the user and the I-sharing friends, and the reliability of I-sharing friends

) And collects and analyzes the user's subjective reliability (

); And a user's subjective reliability for the target device (

Collecting and analyzing the standardized reputation (a _{x, z} ) of the I-sharing buddies for the target device and finally calculating the user's confidence (T _{x, z} ) for the target device Provides a method for calculating user credibility for unfamiliar devices in the Internet environment.

Calculating the subjective experience similarity (sim (p _u , p _x )) of the user and the friend based on the interaction history of the user and the friend in the same situation as the unfamiliar situation; And filtering only the buddies whose subjective experience similarity (sim (p _u , p _x )) is equal to or greater than a predetermined filter value to the I-sharing buddies.

And the interaction history includes a plurality of reliability information corresponding to each of a plurality of situations.

"의 식에 따라 계산하며, 상기 a_i와 b_i는 상황 i에 대한 사용자와 친구의 신뢰도이고, 상기 n는 비교될 공통 경험 집합의 크기인 것을 특징으로 한다. The step of calculating the subjective experience similarity (sim (p _u , p _x )) of the user and the friends is performed by calculating the subjective experience similarity (sim (p _u , p _x )

, Where a _i and b _i are the confidence of the user and friend for situation i and n is the size of the common experience set to be compared.

The step of filtering the I-sharing friend may include repeating the I-sharing friend filtering operation while sequentially decreasing the filter value when the I-sharing friend is insufficient, thereby increasing the number of the I-sharing friends And further comprising:

)를 계산하는 단계는 상기 사용자의 주관적 신뢰도(

)는 "

"의 식에 따라 계산하며, 상기 n는 상기 I-sharing 친구들의 수인 것을 특징으로 한다. The user's subjective reliability (

) Of the user's subjective reliability (

) Is "

Quot ;, and n is the number of the I-sharing buddies.

"의 식에 따라 계산하며, 상기 sf_x,z는 주관적 경험 유사도 필터 값인 것을 특징으로 한다. The final step of calculating the reliability of the users (T _{x, z)} is the reliability of the user for the target device (T _{x, z)} is "

, Where sf _{x, z} is a subjective experience similarity filter value.

In addition, the final reliability of the user (T _{x, z)} the step of calculating the subjective experience similarity filter value (sf _{x, z)} formal friend x about a user's subjective reliability and subject device z to the subject device through the And adjusting the specific gravity of the standard flat plate.

), 상기 대상 기기에 대한 I-sharing 친구들의 정형화된 표준 평판(a_x,z)을 수집 및 분석하여 상기 대상 기기에 대한 사용자의 신뢰도(T_x,z)를 계산하는 신뢰도 계산부를 포함하는 사물 인터넷 환경에서 낯선 기기에 대한 사용자 신뢰도 계산 시스템을 제공한다. According to another aspect of the present invention, there is provided a method for calculating subjective experience similarity by comparing past experience of a user and friends on a situation basis, calculating the subjective experience similarity, a subjective experience similarity calculator for selecting as a sharing friend; And the subjective experience similarity (sim (p _u , p _x )) between the user and the I-sharing friends, the reliability of I-sharing friends

), A reliability calculator for collecting and analyzing the standardized reputation (a _{x, z} ) of I-sharing buddies for the target device and calculating the reliability (T _{x, z} ) of the user for the target device The present invention provides a system for calculating a user's trust in an unfamiliar device in an Internet environment.

The system further comprises a database for storing and managing the interaction history of the user and the friend.

The system may further include a flat panel search unit for searching and providing a standard flat plate of the target device.

The user reliability calculation system and method for unfamiliar devices in the internet environment of the present invention can evaluate the user's reliability of the target device in an unfamiliar place by utilizing the subjective experience of the I-sharing friend so that the RMSE can be dramatically reduced do.

Also, by selecting an I-sharing friend based on the subjective experience similarity with the user, the possibility of interaction with a potential risky person is prevented.

FIG. 1 is a diagram for explaining a system for calculating a user trust for an unfamiliar device in an Internet environment of objects according to an embodiment of the present invention. Referring to FIG.
FIG. 2 is a flowchart illustrating a method of calculating a user trust for a device unfamiliar in an object Internet environment according to an exemplary embodiment of the present invention. Referring to FIG.
3 is a diagram illustrating an example of subjective experience similarity calculation according to an embodiment of the present invention.
4 is a diagram illustrating an example of subjective reliability calculation according to an embodiment of the present invention.
5 is a diagram illustrating an example of user final confidence calculation according to an embodiment of the present invention.
FIG. 6 is a diagram for explaining an effect of a method for calculating a user's trust in an unfamiliar device in an object Internet environment according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The above and other objects, features and advantages of the present invention will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: FIG. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

The following terms are defined in consideration of the functions of the present invention, and these may be changed according to the intention of the user, the operator, or the like.

The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art to which the present invention pertains. Only. Therefore, the definition should be based on the contents throughout this specification.

FIG. 1 is a diagram for explaining a system for calculating a user trust for an unfamiliar device in an Internet environment of objects according to an embodiment of the present invention. Referring to FIG.

), 상기 대상 기기에 대한 I-sharing 친구들의 정형화된 표준 평판(a_x,z)을 수집 및 분석하여 상기 대상 기기에 대한 사용자의 신뢰도(T_x,z)를 계산하는 신뢰도 계산부(20), 사용자 및 친구의 인터렉션 이력을 저장 및 관리하는 데이터베이스(30), 외부의 평판 관리 시스템 등에 접속하여, 대상 기기의 정형화된 표준 평판을 검색 및 제공하는 평판 검색부(40) 등을 포함할 수 있다. Referring to FIG. 1, the system of the present invention calculates a subjective experience similarity by comparing an interaction history between a user and a friend on a situation basis, and calculates subjective experience similarity by selecting friends who are equal to or greater than a predetermined filter value as I- A similarity calculation unit 10, a subjective experience similarity sim (p _u , p _x ) between the user and the I-sharing friends, a reliability of I-sharing friends with respect to the target device

A reliability calculation unit 20 for collecting and analyzing the standardized reputation (a _{x, z} ) of the I-sharing buddies for the target device and calculating the reliability (T _{x, z} ) of the user for the target device, A database 30 for storing and managing the interaction history of users and friends, and a flat panel search unit 40 for searching and providing a standardized flat plate of the target device by accessing an external flat management system or the like .

That is, the present invention assumes that the subjective experience of the user can be replaced by the experience of the I-sharing friend, which is the experience of the user having the subjective experience similarity with the user, So that it is possible to estimate the user's subjective reliability. At this time, the experience may consist of contextual contexts (e.g., device type, device role, location information, interaction, etc.) that affect the reliability of the interaction.

Hereinafter, a method of calculating the user's trust in an unfamiliar device in the object Internet environment will be described with reference to FIGS. 2 to 5. FIG.

FIG. 2 is a flowchart illustrating a method of calculating a user trust for a device unfamiliar in an object Internet environment according to an exemplary embodiment of the present invention. Referring to FIG.

Referring to FIG. 2, the method of the present invention includes a step S 1 of calculating subjective similarity, an I-sharing friend filtering step S 2, a user's subjective reliability calculation step S 3 of the target device, (Step S4).

First, the subjective experience similarity calculation step of S1 is performed as follows.

The present invention finds an I-sharing friend of a user and calculates the reliability of the user by using opinions of I-sharing friends. Accordingly, the subjective experience similarity calculation unit 10 extracts the interaction history of the user and the friend based on the situation basis to find a friend who can complement the subjective experience of the target device. Here, the interaction history includes a plurality of reliability information corresponding to each of a plurality of situations. This is because the user assumes that he / she is in an unfamiliar place and has never experienced the target device directly.

Therefore, devices of the same type are expected to share the same attributes (e.g., objective, intelligent service provision, etc.) and have their experience for devices of the same type extracted instead.

Then, the subjective experience similarity calculation unit 10 uses Equation (1) to measure how similar subjective experience similarity of users and friends is in the same situation. The degree of similarity can be measured as the average of experience differences in the same situation.

[Equation 1]

Here, u, x, and sim (p _u , p _s ) represent subjective experience similarities between users, friends, and users and friends. n is the size of the common experience set to be compared, and a _i and b _i are the confidence values of the user and friend for the situation i, and are numeric values of 0 to 1 inclusive.

Since people have different sets of experience, we try to compare the subjective experience similarity with the experiences of users and friends at the same time.

3 shows an example of subjective experience similarity calculation according to an embodiment of the present invention, wherein user u experiences device type DT in situations S _A , S _B and S _C , If friend x and friend y experience device type DT in situations S _A , S _B , S _C and S _D , then based on the confidence of user u and friend x for situations S _A , S _B , and S _C , friends x of subjective experience similarity (sim (p _u, p _x)) to, conditions S _a, S _B, user u and subjective experience similarity friend y user u and friends x based on the reliability of the S _C (sim (p _u , p _x )). Then, considering the fact that a _i and b _i are numerical reliability between [0, 1], the subjective experience similarity final value is calculated by subtracting the difference average from 1. In Equation (1), a friend having a high degree of subjective experience with a user has a high subjective experience similarity.

Then, the I-sharing friend filtering step of S2 is performed as follows.

The subjective experience similarity calculation unit 10 calculates a subjective experience similarity, and then generates a friend list in which the subjective experience similarity calculation result is stored.

However, in order to change the user's subjective experience more precisely, only the friend with the same subjective experience as the user should be considered.

Therefore, a subjective experience similarity filter should be established to filter out friends with low subjective similarity. The subjective experience similarity filter value acts as a threshold. And only those friends with subjective experience similarity greater than the subjective experience similarity filter value are selected as I-sharing friends.

The subjective experience filter determines the number of experiences of a friend to replace the user experience. The lower the subjective subjective similarity filter value, the more the friend's experience to replace the user's subjective experience, but the more likely the user's experience will be replaced by the user's subjective experience and the different experience. The subjective experience similarity filter defines how much the I-sharing friend is equal to the user. Therefore, the subjective experience similarity filter determines the number of I-sharing buddies, which is an important factor in user's experience substitution.

However, if the number of I-sharing friends is too small, the number of experiences with the target device is reduced. In the present invention, therefore, at least four shared friends are required to calculate accurate subjective reliability.

To find a friend who can better replace the user experience, you should set the subjective experience similarity filter value as high as possible. However, if we do not find enough friends who satisfy the subjective experience similarity filter, we reduce the subjective experience similarity filter value by the preset value and then repeat the I-sharing friend filtering operation to secure enough I-sharing friends with high subjective experience .

The user's subjective reliability calculation step for the target device of S3 is performed as follows.

When the subjective experience similarity calculation of S1 and the I-sharing friend search of S2 are completed, the subjective experience similarity calculation unit 10 transmits the calculated subjective experience similarity and the I-sharing friend list to the reliability calculation unit 20. The reliability of the delivered I-sharing buddies is added to complement the immaturity of the user's personal experience. In doing so, the basic principle is that the higher the similarity of subjective experiences of I-sharing friends, the more the subjective experience of I-sharing friends should be reflected.

를 계산하도록 한다. The reliability calculation unit 20 calculates the subjective reliability of the user u with respect to the target device z by using the following equation (2)

.

&Quot; (2) "

는 대상 기기 z에 대한 친구 x의 주관적 경험을 의미하는 직접 신뢰도이고, n은 친구의 수이다. At this time, the subjective reliability

Is the direct confidence in the subjective experience of friend x on the target device z, and n is the number of friends.

When the friend's subjective experience similarity is multiplied by the friend's trust, the weight is calculated. This allows us to distinguish the importance of a friend's opinion when estimating the final reliability of the user.

FIG. 4 illustrates an example of subjective reliability calculation according to an embodiment of the present invention. Referring to FIG. 4, the reliability of friend x (0.33 ) To obtain the weight of friend x and multiply the subjective experience similarity degree (0.67) of friend y by the reliability (0.33) of friend y to the object device z to obtain the weights of friend y and then averages them to obtain 0.35 It can be seen that the user u's subjective reliability with respect to the device z can be obtained.

Finally, the final reliability calculation step for the target device of S4 is performed as follows.

The reliability calculation unit 20 of the present invention calculates the final reliability using the user's subjective reliability based on the following Equation (3).

&Quot; (3) "

는 대상 기기 z에 대한 사용자 u의 최종 신뢰도를 의미하고, 이는 대상 기기 z에 대한 사용자 u의 주관적 신뢰도

와 대상 기기 z에 대한 친구 x의 정형화된 표준 평판

이라는 두 가지 요소 기반으로 계산된다. In Equation 3,

Denotes the final reliability of the user u with respect to the target device z, which means that the user u's subjective reliability

And the standardized reputation of friends x for the target device z

Based on two factors.

은 일반 대중이 대상 기기를 어떻게 생각하는지 객관적인 신뢰도이다. 주관적 경험 유사도 필터 값

는 주관적 신뢰도와 정형화된 표준 평판을 조화시키는 데 균형 계수로써 이용된다. 즉, 주관적 경험 유사도 필터 값

을 통해 대상 기기에 대한 사용자의 주관적 신뢰도와 대상 기기 z에 대한 정형화된 표준 평판의 고려 비중을 조정할 수 있다. Formal standard reputation

Is an objective measure of how the public thinks of the target device. Subjective experience similarity filter value

Is used as a balancing factor to balance subjective reliability and formal standard reputation. That is, the subjective experience similarity filter value

It is possible to adjust the proportion of consideration of the user's subjective reliability for the target device and the standardized reputation for the target device z.

The subjective experience similarity filter is proportional to subjective experience similarity of subjective experience of friends. If the subjective experience similarity filter value is high, the user has a high subjective experience similarity and experiences that can replace the user's experience. If so, users are more likely to rely on subjective values rather than objective values.

와 사용자와 친구간 주관적 경험 유사도 sim(p_u, p_z)를 기반으로 0.481이라는 대상 기기 z에 대한 사용자 u의 주관적 신뢰도(

)를 산출한 후, 0.31이라는 대상 기기 z에 대한 정형화된 표준 평판

과, 0.80이라는 주관적 경험 유사도 필터 값을 이용하여, 0.54라는 사용자 최종 신뢰도를 계산할 수 있음을 알 수 있다. (발명자 검토 요청 사항 : 도 5의 opinion, SF를

및 sim(p_u, p_z)로 교체하였습니다. 상기 교체가 바람직한 것인지 확인 부탁드립니다. FIG. 5 is a diagram illustrating an example of user final reliability calculation according to an embodiment of the present invention. Referring to FIG. 5,

And the user's subjective reliability (p _u , p _z ) of user u to the target device z of 0.481 based on the subjective experience similarity sim (p _u , p _z )

), And then a standardized standard reputation for the target device z of 0.31

And a subjective experience similarity filter value of 0.80, it is possible to calculate a user final reliability of 0.54. (Inventor review request: Opinion of Figure 5, SF

And sim (p _u , p _z ). Please confirm whether the above replacement is preferable.

FIG. 6 is a diagram for explaining an effect of a method for calculating a user's trust in an unfamiliar device in an object Internet environment according to an embodiment of the present invention.

In order to demonstrate that the method of the present invention improves the accuracy of the reliability estimation, the method of the present invention and Son is compared in terms of RMSE.

If the present invention has an RMSE, it can be concluded that an I-sharing friend may be helpful in improving objective reliability when there is no direct interaction history. Considering that the subjective experience similarity level affects the reliability estimation result, we divide the users into four groups based on the subjective experience similarity filter value, and analyze how the present invention improves the estimation accuracy to some extent.

The SF <70 group is a set of users whose similarity is less than 0.70. That is, all users in this group should reduce the subjective experience similarity filter value to 0.70 or less to find four I-sharing friends. The subjective experience similarity filter value of group SF <75 is greater than 0.70 and less than 0.75, the value of group SF <80 is less than 0.75 and less than 0.80, and the group SF <80 is greater than 0.80. Ultimately, users of Group 80 <SF have an I-sharing friend with the highest subjective experience similarity.

FIG. 6 shows how the method of the present invention and Son's RMSE result are distributed. Referring to FIG. 6, the RMSE value of the present invention tends to decrease with subjective experience similarity, but Son maintains a constant value. Also, it can be seen that the present invention has better performance than Son for all subjective experience similarities. The RMSE of Son is about 0.18, but the maximum RMSE of the present invention is about 0.12. This means that the I-sharing friend's experience effectively replaces the user's subjective experience.

The decreasing tendency of the RMSE value of the present invention supports the basic assumption of the present invention that the higher the degree of similarity between the user and the I-sharing friend is, the more effective the experience replacement is. The reason that the last RMSE remains the same from group SF <70 to group 80 <SF depends on the standardized reputation only if there is no direct experience.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are intended to illustrate rather than limit the scope of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

Claims (11)

Comparing the history of interaction between the user and the friend on the basis of the situation, and selecting friends who are highly similar to the subjective experience as the I-sharing friend;
The subjective experience similarity (sim (p _u , p _x )) between the user and the I-sharing friends, and the reliability of I-sharing friends

) And collects and analyzes the user's subjective reliability (

); And
The subjective reliability of the user for the target device

Collecting and analyzing the standardized reputation (a _{x, z} ) of the I-sharing buddies for the target device and finally computing the user's confidence (T _{x, z} ) for the target device; ,
The step of selecting the I-sharing friend
Calculating subjective experience similarity (sim (pu, px)) between users and friends based on the interaction history of users and friends in the same situation as the unfamiliar situation; And
And filtering only the buddies whose subjective experience similarity (sim (pu, px)) is equal to or greater than a predetermined filter value to an i-sharing buddy. delete 2. The method of claim 1,
Wherein the reliability information includes a plurality of reliability information corresponding to a plurality of situations. 4. The method of claim 3, wherein calculating the subjective experience similarity (sim (p _u , p _x )) of the user and the friends
The subjective experience similarity (sim (p _u , p _x )) of the user and the friends is defined as "

&Quot; and &quot;
Wherein a _i and b _i are confidence of a user and a friend for a situation i and n is a size of a common experience set to be compared. 2. The method of claim 1, wherein filtering
Further comprising the step of repeatedly performing an I-sharing friend filtering operation while sequentially decreasing the filter value when the I-sharing friend is insufficient, thereby increasing the number of I-sharing buddies A method for calculating user credibility for an unfamiliar device. The method of claim 1, wherein the subjective reliability of the user

),
The user's subjective reliability (

) Is "

&Quot;, wherein n is the number of the I-sharing buddies. 2. The method of claim 1, wherein the step of finally calculating the user's confidence (T _{x, z} )
The user's confidence (T _{x, z} ) for the target device is &quot;

, Wherein sf _{x, z} is a subjective experience similarity filter value. The method of claim 1, 8. The method according to claim 7, wherein the step of finally calculating the reliability (T _{x, z} )
And adjusting the proportion of consideration of the user's subjective reliability for the target device and the standardized reputation of the friend x for the target device z through the subjective experience similarity filter value (sf _{x, z} ) A method for calculating user credibility for unfamiliar devices in an Internet environment. A subjective experience similarity calculating unit for calculating subjective experience similarity by comparing past experience of a user and friends based on a situation basis and selecting friends who have the subjective experience similarity equal to or higher than a predetermined filter value as an I-sharing friend; And
The subjective experience similarity (sim (p _u , p _x )) between the user and the I-sharing friends, the reliability of I-sharing friends

And a reliability calculation unit for collecting and analyzing the standardized reputation (a _{x, z} ) of the I-sharing buddies of the target device to calculate the reliability (T _{x, z} ) of the user for the target device,
The subjective experience similarity calculation unit
(Sim (pu, px)) of the user and friends based on the interaction history of the user and the friends in the same situation as the unfamiliar situation, and the subjective experience similarity (sim (pu, px) And filtering only the buddies having the filter value or more as an I-sharing buddy. 10. The method of claim 9,
Further comprising a database for storing and managing an interaction history of the user and the friend. 10. The method of claim 9,
Further comprising a flatbed retrieval unit for retrieving and providing a formatted standard reputation of the target device.

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