An Efficient Privacy Management System in Online Social ...

An Efficient Privacy Management System in

Online Social Networks

T. Sureshkumar

(ASP/IT) Department of Information Technology

Nandha College of Technology

Erode, India.

K. Ganesh, K. Manikandan,

S. Suryapprakash, V. Renugapriya

(UG Scholar) Department of Information Technology

Nandha College of Technology

Erode, India

Abstract: Online social networks (OSNs) have experienced

tremendous growth in recent years and become a defect portal for

hundreds of millions of Internet users. These OSNs offer attractive

means for digital social interactions and information sharing, but

also raise a number of security and privacy issues. While OSNs

allow users to restrict access to shared data, they currently do not

provide any mechanism to enforce privacy concerns over data

associated with multiple users on.

Keywords: Individual security, Osn (Online Social Network),

UCB (Upper Confidence Bound)

I. INTRODUCTION

Novel Topic-Sensitive Influencer Mining (TSIM)

framework in interest-based social media networks. TSIM

aims to find topical influential users and images. The

influence estimation is determined with a hyper graph

learning approach. In the hyper graph, the vertices represent

users and images, and the hyper edges are utilized to capture

multitier relations including visual-textual content relations

among images, and social links between users and images.

Algorithm wise, TSIM first learns the topic distribution by

leveraging user-contributed images, and then infers the

influence strength under different topics for each node in the

hyper graph. We pursue a systematic solution to facilitate

collaborative management of shared data in OSNs. We

begin by examining how the lack of Multi Party Access

Control (MPAC) for data sharing in OSNs can undermine

typical data sharing the protection of user data. Some

patterns with respect to multiparty authorization in OSNs

identified. Based on these sharing patterns, an the core

features of are also MPAC model is formulated to capture

multiparty authorization requirements that have not been

accommodated so far by existing access control systems Our

control and models for OSNs. Model also contains a

multiparty policy specification scheme. Meanwhile, since

conflicts are inevitable in multi-party authorization

enforcement, a voting mechanism is further provided to deal

with authorization and privacy conflicts in our model.

II. LITERATURE SURVEY

(i) Hypergraph Learning With Hyper edge Expansion

Many tasks require clustering in a graph where each edge

represents a similarity relation. Often, it is a co-occurrence

relation that involves more than two items, such as the co-

citation and co-purchase relations. The co-occurrence

relation can be represented by a hyperedge that connects

two or more vertices in a hyper graph. Therefore, hyperedge

relations are often transformed into another graph that is

easier to handle. For classification and clustering tasks, the

hyperedge are usually transformed into cliques of edges.

This category of techniques includes clique expansion, star

expansion.With a vertex expansion, evaluating the goodness

of clustering is done on the induced graph. For example, in a

hyperedge of k vertices, a cut that separates the hyperedge

into 1 and k − 1 vertices would cut k − 1 pairwise edges,

while a cut that splits the vertices in two equal halves would

have k 2/4 cut edges. Thus the vertex expansion would

prefer an unbalanced clustering. To mitigate the problem of

unbalanced clustering, it is proposed in star expansion and

NHC to use the cluster volume as a normalizer for balancing

the cluster sizes. But such normalization cannot completely

eliminate the problem. We present the following example of

vertex embedding to explain why the problem still exists.

By computing the eigenvectors of the normalized Laplacian

LN HC of the induced graph, it is possible to project the

vertices into a Euclidian space, which is called embedding

in spectral graph learning. On the left side of Figure 1, we

show the 1-dimensional vertex embedding of NHC by the

eigenvector corresponding to the second smallest eigenvalue

of LN HC. It is worth to focus on the vertices that belong to

both hyperedges (the overlapping part). Although the

hyperedges have the same weight and the cluster volume

normalizer is applied, the overlapping part is still biased to

the side with less vertices (in this case e2 side). This means

that the optimal clustering of two clusters should assign the

overlapping part and other vertices in e2 to one cluster. Such

bias might be a problem when the hyperedge sizes are

unbalanced, e.g. co-citation relations with a lot or a few

citations. Moreover, the behavior of the artificial

normalization (or “correction”) could be undesirable when

many hyperedges intersect with each other, because the cost

of the clustering would depend on how a hyperedge is split

into the clusters. An even split would introduce a different

cost compared to an uneven split. As any hyperedge that is

not entirely within the same cluster represents a relation that

is violated by the clustering, it would be natural to have the

learning result independent of the hyperedge sizes and only

depend on the hyperedge connectivity and hyperedge

weights. A presented a new transformation called hyperedge

expansion (HE) based on a network flow technique so that

the learning result is invariant to the distribution of vertices

among hyperedges. HE expansion is first carried out on the

hyperedge level. Then the learning results on hyperedges are

projected back to the vertices through the adjacency

information between hyperedges and vertices.

International Journal of Engineering Research & Technology (IJERT)

ISSN: 2278-0181

Published by, www.ijert.org

RTICCT - 2019 Conference Proceedings

Volume 7, Issue 01

Special Issue - 2019

1

www.ijert.org

(ii) Learning a Hidden Hypergraph

Is introduced an interesting combinatorial object, which we

call an independent covering family. Basically, an

independent covering family of a hypergraph is a collection

of independent sets that cover all non-edges. An interesting

observation is that the set of negative queries of any

algorithm that learns a hypergraph drawn from a class of

hypergraphs that is closed under the operation of adding an

edge is an independent covering family of that hypergraph.

Note both the class of r-uniform hypergraphs and the class

of (r, ∆)-uniform hypergraphs are closed under the operation

of adding an edge. This implies that the query complexity of

learning such a hypergraph is bounded below by the

minimum size of its independent covering families. In the

opposite direction, subroutines are given one arbitrary edge

from a hypergraph. With the help of the subroutines, we

show that if are constructed small-sized independent

covering families for some class of hypergraphs, It is able to

obtain an efficient learning algorithm for it. In this paper, we

give a randomized construction of an independent covering

family of size O(r2 2r m log n) is given for r-uniform

hypergraphs with m edges. This yields a learning algorithm

using a number of queries that is quadratic in m, which is

further improved to give an algorithm using a number of

queries that is linear in m. As mentioned in Anglin and Chen

(2004) and some other papers, the hypergraph learning

problem may also be viewed as the problem of learning a

monotone Disjunctive Normal Form (DNF) Boolean

formula using membership queries only. Each vertex of H is

represented by a variable and each edge by a term

containing all variables associated with the vertices of the

edge. A membership query assigns 1 or 0 to each variable,

and is answered 1 if the assignment satisfies at least one

term, and 0 otherwise, that is, the set of vertices

corresponding to the variables are assigned 1 of contains all

vertices of at least one edge of H. An r-uniform hypergraph

corresponds to a monotone r-DNF. An (r, ∆)-uniform

hypergraph corresponds to a monotone DNF whose terms

are of sizes in the range of [r − ∆, r]. Thus, our results apply

also to learning the corresponding classes of monotone

DNF.

Formulas Using Membership Queries.

In this section, algorithm is given that finds an arbitrary

edge in a hypergraph of dimension r using only r log n edge-

detecting queries. The algorithm is adaptive and takes r log

n rounds. The success probability in the construction of

independent covering families in the previous section can be

easily improved by drawing more samples. Using the high-

probability version of the construction, algorithm is obtained

using a number of queries that is quadratic in m that learns

an r-uniform hypergraph with m edges with high

probability. Although the first algorithm for finding one

edge is deterministic and simple, the round complexity r log

n might be too high when n is much larger than m. The

round complexity to O(log m + r) is improved using only

O(log m log n) more queries.

(iii) Image Retrieval Via Probabilistic Hypergraph

Ranking

Hypergraph based transductive algorithm is proposed to the field of image retrieval. Based on the similarity matrix computed from various feature descriptors, image is takenas a ‘centroid’ vertex and form a hyperedge by a centroid and its k-nearest neighbors. To further exploit the correlation information among images, Is proposed a novel hypergraph model called the probabilistic hypergraph, which presents not only whether a vertex vi belongs to a hyperedge ej, but also the probability that vi ∈ ej . In this way, both the higher order grouping information and the local relationship between vertices within each hyperedge are described in this model. To improve the performance of content-based image retrieval, the hypergraph-based transductive learning algorithm is proposed in to learn beneficial information from both labeled and unlabeled data for image ranking. After feedback images are provided by users or active learning techniques, the hypergraph ranking approach tends to assign the same label to vertices that share many incidental hyperedges, with the constraints that predicted labels of feedback images should be similar to their initial labels.

The contribution of this paper is threefold:

• A proposed a new image retrieval framework based

on transductive learning with hypergraph structure,

which considerably improves image search

performance;

• A probabilistic hypergraph model to exploit the

structure of the data manifold by considering not

only the local grouping information, but also the

similarities between vertices in hyperedges;• An in depth comparison between simple graph and

hypergraph based transductive learning algorithms

is conducted in the application domain of image

retrieval, which is also beneficial to other computer

vision and machine learning applications.

It presents an active learning framework, in which a fusion

of semi-supervised techniques (based on Gaussian fields and

harmonic functions) and SVM are comprised. And pairwise

graph based man if old ranking algorithm is adopted to build

an image retrieval system. Cain et al. put forward semi-

supervised discriminant analysis and active subspace

learning to relevance feedback based image retrieval. In a

simple graph both labeled and unlabeled images are taken as

vertices; two similar images are connected by an edge and

the edge weight is computed as image-to-image affinities.

Depending on the affinity relationship of a simple graph,

semi-supervised learning techniques could be utilized to

boost the image retrieval performance.

(iv) User Interest And Social Influence Based Emotion

Prediction For Individuals

Emotions are playing significant roles in daily life, making

emotion prediction important. To date, most of state-of-the

art methods make emotion prediction for the masses which

are invalid for individuals. Is proposed novel emotion

prediction method for individuals based on user interest and

social influence. To balance user interest and social

influence, Is proposed a simple yet efficient weight learning

method in which the weights are obtained from users’

behaviors.


ISSN: 2278-0181



Volume 7, Issue 01


2

www.ijert.org

The problem of emotion prediction for individuals is not

trivial. So far, there are fewer works on emotion prediction

for individuals. Emotions have long been viewed as

passions produced on their own interest. However, from

social as- pact, has shown that how happy users is

influenced are users social links to people in social

networks. More recently, Tang’s work quantitatively studies

how an individual’s emotion is influenced by his friends in

social network .It can be seen from the above the existing

emotion prediction methods for individuals either focus on

user interest or social influence. However, neither user

interest nor social influence alone can predict individual’s

emotion accurately. Is proposed a novel method jointly

considering user interest and social influence in social

network platform to predict user’s emotion. Is proposed a

simple yet efficient weight learning method to balance the

weights of user interest and social influence to figure out

exactly what kind of roles they are playing in the final

emotion prediction. The conceptual framework of our

proposed emotion prediction for individuals. With the

popularity of social network, i.e. Facebook, Twitter and

Flicker, more and more people are willing to share their own

feelings towards hot events or their experiences in daily life,

whether delivering positive or negative emotions, which

makes it easier for people to know others’ minds. More or

less, users are getting increasingly easier to be influenced by

others in social network. However, different friends may

have different extents of influences on the user based on

how close they are or how much similarity they have in

common. The use the emotion similarity when treated the

same microblog to measure the social influence.

III. EXISTING SYSTEM

Cryptographic mechanism-based security social media

technology mainly uses cryptographic security techniques

for groups with dynamic memberships

The group is any community or any cluster which shows

same properties. The social media problems of security,

privacy and anti-piracy can be overcome through

cryptographic techniques like authentication, encryption etc.

• The user needs to store many community keys if

he/she belongs to several communities

• There does not exist an efficient way to revoke the

member permanently or temporarily

• There does not exist an efficient way for

anonymous authentication with the view of tracing

the behaviour of users and computer forensics.

Each tag is an explicit reference that links to a user’s space.

For the user data, current OSNs indirectly require for

regulating protection of users to be system and policy

administrators their data, where users can restrict data

sharing to a specific trusted users. OSNs often use user

relationship and between trusted and set of group

membership to distinguish untrusted users. For example, in

Facebook, users can allow friends, Friends of Friends

(FOF), groups, or public to access their personal

authorization and data, depending on privacy requirements.

Although OSNs currently provide simple access control

mechanisms allowing users to govern access to information

have no contained in their own spaces, users, unfortunately,

Control over data residing outside their spaces. For instance,

if a user posts a comment in a friend’s space, she/he cannot

specify which users can view the comment.

In another case, when a user uploads a photo and tags

friends who appear in the photo, the tagged friends cannot

restrict who can see may have this photo, even though the

tagged friends different privacy concerns about the photo.

To address such a critical issue, preliminary protection

mechanisms have been offered by existing OSNs.

IV. PROBLEM DEFINITION:

• Multi-armed bandit problem

• Detecting the conflicts among different users’

privacy policies, and then generating an aggregated

policy that can resolve the conflicts to the largest

extent collaborative privacy management in OSNs

• Aggregated policy may cause a privacy loss to

some of the users

Detection Strategy

Classification: Classification means researchers first to

build a model for a group of classes or concepts, then use

the model to predict class labels for test data. For example,

to classify whether an email is email spam, web page is web

spam.

Prediction: Prediction focuses on the continuous-valued

functions of models researchers created. For example,

scientists use the prepared models to forecast the economic

growth in the next year.

Classification and prediction are a two-step process, which

means, model construction and model applications. Model

construction means scientists first need to introduce a set of

predefined classes, which called training dataset. Training

dataset consists of tuples for building a model, and each

tuple or sample belongs to a predefined class. At the same

time, researchers need to make the classification rules,

classification models, decision trees, decision rules, or math

formulae, etc. Model application means to classify those

unseen objects: researchers need to use an independent test

data set to estimate the accuracy of the model, then use the

model to classify unknown class labels. The training dataset

needs to use some features to make a further application. As

former researchers’ experience, most of the features are web

page top domains, languages, some words (body and title),

average word length, anchor words, visibility of content,

repeating keywords, the most common keywords, n-gram

likelihood and so on. Suppose to explore the influence of

spam in one OSN to another; we do not aim to show how

great performance of detection only around one dataset. So

we chose 10% original data to do the training work so that it

can maintain the maximum independence and testability of

posts in one social network, at the same time, it is more

intuitional and beneficial to show the influence of spam

related with same topics in other social network to the spam

detection in that social network.

The strategy of process are as follows:

A. We first split TSD and FSD separately into

training and test datasets, use training datasets

to train the various classifiers, and then use

classifiers to check the test datasets. We then

get the original classified results of Twitter

and Facebook Spam Dataset.


ISSN: 2278-0181



Volume 7, Issue 01


3

www.ijert.org

B. To show the influence of Facebook spam

posts in Twitter spam tweets classification,

this research combines spam of Facebook into

a Twitter training set; we then use the newly

trained classifiers to test the remaining

dataset.

C. After step 2, we then do the same procedure in

the Facebook training dataset, and then apply

the new training process to verify the original

test dataset.

D. Finally, we combine the results of

classifications on the above two social

networks.

E. And finally got the exact result and determine

given the statement about the process.

Combined filtered classifier is used to train and test

with various classify algorithms and String to

Word Vector to process natural language. We also

use precision, F1-Measure as criteria to evaluate

the classification performance. The relations of the

true positive (TP), true negative (TN), false

positive (FP) and false negative (FN) are shown.

True positive: Facebook users correctly identified

as Facebook users; False positive: Twitter users

incorrectly identified as Facebook users; True

negative: Twitter users correctly identified as

Twitter users; False negative: Facebook users

incorrectly identified as Twitter users. In general,

Positive means identified, and negative means

rejected. Therefore: True positive means correctly

identified; false positive means incorrectly

identified; True negative means correctly rejected;

false negative means incorrectly rejected.


ISSN: 2278-0181



Volume 7, Issue 01


4

www.ijert.org

V. PROPOSED SYSTEM:

A high threshold indicates that the user has a relatively low

tendency to share the data with others, and only when the

majority of the involved users or users that are highly trusted

agree to post the data, the data can finally be posted. By

tuning the threshold, the user can make a trade-off between

data sharing and privacy preserving. A trust-based

mechanism is proposed for collaborative privacy

management in OSNs. The trust values between users are

associated with users’ privacy loss, and the proposed

mechanism can encourage users to be more considerate of

other users’ privacy. Trust-based privacy management

mechanism based on threshold which the user makes the

final decision on data posting. A high threshold indicates

that the user has a relatively low tendency to share the data

with others, and only when the majority of the involved

users or users that are highly trusted agree to post the data,

the data can finally be posted. By tuning the threshold, the

user can make a trade-off between data sharing and privacy

preserving. A bandit approach is proposed to adjust the

parameter of the trust-based mechanism. By applying the

UCB policy, the user can make a rational trade-off between

data sharing and privacy preserving.

Algorithm:

1: for t = 1 to K do

2: Choose arm It = t

3: Observe and record the reward rIt ,t

4: r ← rIt ,t

5: nIt ← 1

6: end for

7: for t = K + 1 to T do

8: for i = 1 to K do

9.←1/ni ∑ r 1(I=i)

10: end for

12: Observe and record the reward rIt ,t

13: rt ← rIt ,t

14: nIt ← nIt+ 1

15: end forVI. MODULES

(i) User Profile Creation

A user profile (user profile, or simply profile when used in-

context) is a collection of personal data associated to a

specific user. A profile refers therefore to the explicit digital

representation of a person's identity. A user profile can also

be considered as the computer representation of a user

model. A user profile is a visual display of personal data

associated with a specific user, or a customized desktop

environment. A profile refers therefore to the explicit digital

representation of a person's identity. A user profile can also

be considered as the computer representation of a user

model. A profile can be used to store the description of the

characteristics of person. This information can be exploited

by systems taking into account the persons' characteristics

and preferences. The user personal data store in Online

Social Networks (OSNs) database that details contain

informs like first name, last name, username, password,

email Id, gender etc.

(ii) Post Wall Creation

The Website wall post is the most social network is enabling

with photo sharing activities. Protected albums allow users

to set their albums with access protection. This is one of the

beneficial features from wallpost that who fear with photo

scams on photo sharing websites. Photo tagging the option

makes the photo search easier after a long period of time.

Here ruse can give the names or keywords for photos that

related to the photo in better to recognize easily. Although

OSNs currently provide simple access control mechanisms

allowing users to govern access to information contained in

their own spaces, users, unfortunately, have no control over

data residing outside their spaces. In this module user can

add their or interested photos in their wall. This wall posting

contains the photo, photo description, tag information are

given by the user that details are stored in the OSNs

database.

(iii) Multiparty Policy Access Control (MPAC)

Two steps are performed to evaluate an access request over

MPAC policies. The first step checks the access request

against the policy specified by each controller and yields a

decision for the controller. The accessor element in a policy

decides whether the policy is applicable to a request. If the

user who sends the request belongs to the user set derived

from the accessor of a policy, the policy is applicable and

the evaluation process returns a response with the decision

(either permit or deny) indicated by the effect element in the

policy. Otherwise, the response yields deny decision if the

policy is not applicable to the request. In the second step,

decisions from all controllers responding to the access

request are aggregated to make a final decision for the

access request. Since data controllers may generate different

decisions (permit and deny) for an access request, conflicts

may occur. To make an unambiguous decision for each

access request, it is essential to adopt a systematic conflict

resolution mechanism to resolve those conflicts during

multiparty policy evaluation.

(iv) Topic Distribution Learning

We utilize the image vertices and homogeneous hyper edges

in the hyper graph to learn the topic distribution. We

propose to develop a hyper graph regularized topic model to

fully leverage both content and context information of

images to help learn the potential topics of interest.

However, in real-world scenarios, user-contributed social

media data is inevitably noisy and the textual information

associated with images is usually sparse, which makes it

difficult to use the hyper graph regularized topic model to

learn topics of interest accurately. Therefore, we first select

the informative images with rich tags to identify the latent

topics. Then we obtain the topic distribution for all images

via collaborative representation based similarity

propagation.(v) INFLUENCE RANKING

Topic Sensitive Influence Ranking via Affinity Propagation

Based on the learned topic distribution and the constructed

hypergraph, we perform a topical affinity propagation on the

hypergraph with the heterogeneous hyperedges for

measuring influence regarding topics for each user and

image.


ISSN: 2278-0181



Volume 7, Issue 01


5

www.ijert.org

The affinity propagation algorithm is originally employed

for clustering data to identify a subset of exemplars, which

are used to best account for all other data points by passing

similarity messages between data points. Affinity

propagation can be applied whenever there is a way to

measure or pre-compute a numerical value for each pair of

data points, which indicates how similar they are. In our

scenario, users exert influence on each other through

images, which is reflected in the indirect user behaviors of

favorite or comment links. Users share topical similarity

which can be computed through the connected images and

social links. We can use affinity propagation to exchange

influence messages between users and images along

heterogeneous hyperedges in the hypergraph. The final

derived influence messages between users can be viewed as

mutual social influence between users. The algorithm for

topic-sensitive influence ranking is summarized in

Algorithm 3. In the algorithm, the influence of users and

images is recursively updated until it achieves the optimal

condition.

VII. CONCLUSION

This system can make the privacy control for the individual

user during the sharing process. and efficient user can be

defined which user will be view and will not be view about

their own data. Secure transaction and affinity propagation

is possible for by this set of algorithm. Data privacy can give

businesses a competitive advantage. The general data

production regulation is a challenge but it open up huge

business benefits that boost return on investment, such as

improved customer loyalty and more efficient operation.

Product your content from potential employers and

cybercrime. Allow you to come across as a professional

Individual.

REFERENCES

1. C. Zhang, J. Sun, X. Zhu, and Y. Fang, “Privacy

and security for online social networks: Challenges

and opportunities,” IEEE Netw., vol. 24, no. 4, pp.

13–18, Jul/Aug. 2010.

2. L. Xu, C. Jiang, J. Wang, J. Yuan, and Y. Ren,

“Information security in big data: Privacy and data

mining,” IEEE Access, vol. 2, pp. 1149–1176,

2014.

3. L. Xu, C. Jiang, Y. Chen, J. Wang, and Y. Ren, “A

framework for categorizing and applying privacy-

preservation techniques in big data mining,”

Computer, vol. 49, no. 2, pp. 54–62, Feb. 2016.

4. M. Qiu, K.Gai, and Z. Xiong, “Privacy-preserving

wireless communications using bipartite matching

in social big data,” Future Gener. Comput. Syst., to

be published. [Online]. Available:

http://www.sciencedirect.com/science/article/pii/S0

167739X17301449.

5. C. Fiesler et al., “What (or who) is public?: Privacy

settings and social media content sharing,” in Proc.

ACM Conf. Comput. Supported Cooper. Work Soc.

Comput., Mar. 2017, pp. 567–580.

6. H. Hu, G.-J. Ahn, and J. Jorgensen, “Detecting and

resolving privacy conflicts for collaborative data

sharing in online social networks,” in Proc. 27th

ACM Annu. Comput. Secure. Appl. Conf., Dec.

2011, pp. 103–112.

7. M. Such and N. Criado, “Resolving multi-party

privacy conflicts in social media,” IEEE Trans.

Know. Data Eng., vol. 28, no. 7, pp. 1851–1863,

Jul. 2016.

8. P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-

time analysis of the multigame bandit problem,”

Mach. Learn., vol. 47, no. 2, pp. 235–256, 2002.

9. H. Hu, G.-J. Ahn, Z. Zhao, and D. Yang, “Game

theoretic analysis of multiparty access control in

online social networks,” in Proc. 19th ACM Symp.

Access Control Models Technol., New York, NY,

USA, Jun. 2014, pp. 93–102.

10. H. Hu, G. J. Ahn, and J. Jorgensen, “Multiparty

access control for online social networks: Model

and mechanisms,” IEEE Trans. Knowl. Data Eng.,

vol. 25, no. 7, pp. 1614–1627, Jul. 2013.

.


ISSN: 2278-0181



Volume 7, Issue 01


6

www.ijert.org

An Efficient Privacy Management System in Online Social ...

Documents

Transcript of An Efficient Privacy Management System in Online Social ...