A Two-stage Identification based on Seed and Grow Algorithm against Anonymized Social Networks

Download Full-Text PDF Cite this Publication

Text Only Version

A Two-stage Identification based on Seed and Grow Algorithm against Anonymized Social Networks

Mohammed Yaseen Navalur M S. Nagashree K T

M.Tech, Computer Network Engineering Assistant Professor, Dept of CSE

T.John Institute Of Technology T.John Institute Of Technology Bangalore, India Bangalore, India

Abstract In a internet based social networking services the digital traces left behind even after anonymization they are more sensitive in privacy breaches. The sociologist tracks the feasibility of such an attack. There is an algorithm called Seed and Grow which identifies users from an anonymized social graph based only on graph structure. The algorithm first indentifies a seed sub graph either planted by the attacker or by any one ant then grows the seed layer based on the attackers existing knowledge of the users social relations. The algorithm identifies and relaxes implicit assumptions taken by previous work and improves the identification effectiveness and accuracy.

Index Terms Feasibility, Identification,Privacy and Accuracy,Graph.

I.INTRODUCTION

Social networks like Friendster.com, tagged.com, Xanga.com, LinkedIn including Facebook and Twitter, two popular online social networking services, rank at 2nd and 9th place respectively they have developed on the Internet over the past several years and these social networks have been successful in attracting many users, a decades ago only Telecommunication service providers and Intelligence agencies used to provide the critical informations like date of birth and other user generated contents, now through social networks the users engage with each other for various purposes, including business, entertainment and knowledge sharing. The commercial success of social networks depends on the number of users it attracts, and by encouraging users to add more users to their networks and to share data with other users in the social networks. End users are, however often not aware of the anonymiztion attacks and advertisements.

Due to the strong correlation between use data and the users sociality entity, privacy is a major concerning dealing with social network data in context such as storage, processing and publishing. Privacy control, through which a user can tune the visibility of her profile, is an essential feature in any major social networking service.

Figure 1.1 An illustration of naïve anonymization. Each node represents a user ID attached. Naïve anonymization simply

removes the ID, but retains the network structure.

The common practice for privacy-sensitive social network data publishing is through anonymization,i.e., remove plainly identifying label such as name, social security number, post lore mail address, and retain the structure of the network as published data. Figure1.1 is as implemented illustration of this process. The motivation behind such processing prior to data publishing is that, by removing the who information, the utility of the social networks is maximally preserved with out compromising users privacy. Narayanan and Shmatikov report several high profile cases in which anonymity has been unquestioningly interpreted as equivalent to privacy Can the aforementioned naive anonymization technique each I ever privacy preservation in the context to privacy-sensitive social network data publishing? This interesting and important question was posed only recently by Back strometal. A few privacy attacks have been proposed to circumvent the naïve anonymization protection..

Meanwhile, more sophisticated anonymization techniques

have been proposed to provide better privacy protection [4, 5, 6, 7, 8]. Nevertheless, research in this area is still in its infancy and a lot of work, both in attacks and defenses, remains to be done.

In this paper, we describe a two-stage identification attack, Seed-and-Grow, against anonymized social net-works. The name suggests a metaphor for visualizing its structure and procedure. The attacker first plants a seed into the target social

network before its release. After the anonymized data is published, the attacker retrieves the seed and makes it grow larger, thereby further breaching privacy.

More concretely, our contributions include:

  • We propose an efficient seed construction and re-covery algorithm More specifically, we drop the assumption that the attacker has complete control over the connection between the seed and the rest of the graph (Section 3.1.2); the seed is constructed in a way which is only visible to the attacker (Section 3.1.2); the seed recovery algorithm examines at most the two-hop local neighborhood of each node, and thus is efficient (Section 3.1.3).

  • We propose an algorithm which grows the seed (i.e., further identifies users and hence violates their privacy) by exploiting the overlapping user bases among social network services. Unlike pre-vious works which require arbitrary parameters for probing aggressiveness, our algorithm automat-ically finds a good balance between identification effectiveness and accuracy (Section 3.2).

  • We demonstrate the significant improvements in identification effectiveness and accuracy of our algo- rithm over previous works with real-world social- network datasets.

    1. RELATED WORK

      A natural mathematical model to represent a social net-work is a graph. A graph G consists of a set V of vertices and a set E V

      × V of edges. Labels can be attached to both vertices and edges to represent attributes.

      In this context, privacy can be modeled as the knowl-edge of existence or absence of vertices, edges, or labels. An extension is to model privacy in terms of metrics, such as betweenness, closeness, and centrality, which originate from social network analysis studies [9].

      The naive anonymization is to remove those labels which can be uniquely associated with one vertex (or a small group of vertices) from V . This is closely related to traditional anonymization techniques employed on rela-tional datasets [10]. However, the information conveyed in edges and its associated labels is susceptible to privacy breaches. Backstrom et al. [3] proposed an identification attack against anonymized graph, and coined the term structural steganography.

      Besides privacy, other dimensions in formulating pri-vacy attacks against anonymized social networks, as identified in numerous previous works [5, 6, 8, 11], are the published datas utility, and the attackers background knowledge.

      Utility of published data measures information loss and distortion in the anonymization process. The more information that is lost or distorted, the less useful published data is. Existing anonymization schemes [4, 5, 6, 8, 11] are all based on the trade-off between the usefulness of the published data these graphs, he manages to identify 100 more users from the anonymized graph (the Dissimilarity in-terlude in Section 3.2 illustrates a way to do this). By doing so, Bob

      and the strength of protection. For example, Hay et al. [8] propose an anonymization algorithm in which the original social graph is partitioned into groups before publication, and the number of nodes in each partition, along with the density of edges that exist within and across partitions, are published.

      Although a trade-off between utility and privacy is necessary, it is hard, if not impossible, to find a proper balance overall. Besides, it is hard to prevent attackers from proactively collecting intelligence on the social net-work. It is especially relevant today as major online so-cial networking services provide APIs to facilitate third-part application development. These programming in-terfaces can be abused by a malicious party to gather information about the network.

      Background knowledge characterizes the information in the attackers possession which can be used to compro-mise privacy protection. It is closely related to what is perceived as privacy in a particular context.

      The attackers background knowledge is not restricted to the targets neighborhood in a single network, but may span multiple networks and include the targets alter egos in all of these networks [2]. This is a real-istic assumption. Consider the status quo in the social networking service business, in which service providers, like Facebook and Flickr, offer complementary services. It is very likely that a user of one service would simul-taneously use another service. As a person registers to different social networking services, her connections in these services, which relate to her social relationships in the real world, might reveal valuable information which the attacker can make use of to threaten her privacy.

      The above observation inspires Seed-and-Grow, which exploits the increasingly overlapping user-bases among social networking services. A concrete example is helpful in understanding this idea.

      [Motivating scenario.] Bob, as an employee of a social networking service provider F-net, acquires from his employer a social-network data-set, in which vertices represent users and edges represent private chat sessions. The edges are labeled with attributes such as timestamps. In accordance with its privacy policy, F-net has removed the user IDs from the graph before giving it to Bob.

      Bob, being an inquisitive person, wants to know who these users are. Suppose, somehow, Bob iden-tifies 4 of these users from the graph (the Seed Construction and Seed Recovery interludes in Section 3.1 illustrate a way to do this). By using a graph (with the user ID tagged) he crawled a month ago from the website of another service provider T-net (the 4 identified persons are also users of T- net), and by carefully measuring structural similarity of

      circumvents his employers attempt to protect its customers privacy.

      We conclude this section with a brief comment on our choice of model. We use the undirected graph model to explain the proposed deanonymization attack on social networks. Undirected graphs arise naturally in scenarios where the social relation under investigation is mutual, e.g., friend requests must be confirmed on Facebook. Directed graphs, however, are more appropriate in other cases, such as fans following a movie star on Twitter. An undirected graph could be seen as a special case of directed graphs, in which the relationship is reciprocal; Mislove et al. confirmed the relationship reciprocity in a large-scale study on the Flickr online photo-sharing service [12]. As explained in Section 3, the algorithms used in the proposed deanonymization attack do not rely on the fact that the used graphs are undirected; they work on directed graphs the same way. The undirected graph model is only a choice for specificity and ease of presentation.

    2. SYSTEM MODEL

      SEED-AND-GROW: THE ATTACK

      This section describes an attack that identifies users from an

      anonymized social graph. Let an undirected graph GT = {VT , ET

      } represent the target social network after anonymization. We

      assume that the attacker has an undirected graph GB = {VB , EB } which models his background knowledge about the social

      relationships among a group of people, i.e., VB are labeled with the identities of these people. The motivating scenario

      demonstrates one way to obtain GB .

      The attack concerned here is to infer the identities of the

      vertices VT by considering structural similarity between the

      target graph GT and the background graph GB : Nodes that belong to the same users are assumed to have similar

      connections in GT and GB . Although sporadic connections between who would otherwise be strangers may exist in an online social network (and, thus, affect the similarity between

      GT and GB ), such links can be removed by, for example, quantifying the strength of these connections [13]; the residual network consists of the stable, strong connections that reflect the users real-world social relationships, which give rise to

      the similarity between GT and GB . Additionally, auxiliary

      knowledge about the target graph GT (such as the source and nature of the graph) may help in choosing a background graph

      GB with similar structures.

      Thus, the two graphs GT and GB are syntactically (the social connections) similar but semantically (the meaning associated with such connections) different. By re-identifying

      the vertices in GT with the help of GB , the attacker associates

      the sensitive semantics with users on the anonymized GT and, thus, compromise the privacy of such users. An example of sensitive semantics is the private chat sessions, and their associated timestamps, in the motivating scenario.

      Figure3.1.ArandomlygeneratedgraphGF maybesymmetric.

      VerticesinGF = {v1,…,v5}aredouble-circled.

      We assume that, before the release of GT , the attacker obtains (either by creating or stealing) a few accounts and

      connects them with a few other users (the initial seeds) in GT

      . The feasibility of doing this is the basis of the Sybil identity forgery attack studied in numerous previous works [14, 15, 16, 17, 18, 19, 20, 21, 22]. In-deed, experiments (Section 4) show that our algorithm is capable of identifying 10 times of anonymized users from as few as 5 initial seeds. Besides user IDs, the attacker knows nothing about the relationship

      between the initial seeds and other users in GT . Furthermore, unlike previous works, we do not assume that the attacker has complete control over the connections: the attack only

      knows them before GT s release. This is more realistic. An example is a confirmation-based social network, in which a connection is established only if the two parties confirm it: the attacker can decline but not impose a connection.

      In contrast to a pure structure-based vertex matching algorithm [23], Seed-and-Grow is a two-stage algorithm.

      The seed stage plants (by obtaining accounts and establishing relationships) a small specially designed sub-

      graph GF = {VF , EF } GT (GF reads as fingerprint) into GT before its release. After the anonymized graph is released, the attacker locates GF in GT . The neighboring

      vertices VS of GF in GT are readily identified and serve as the initial seeds to be grown.

      The grow stage is essentially comprised of a structure- based vertex matching, which further identifies vertices

      adjacent to the initial seeds VS . This is a self-reinforcing process, in which the seeds grow larger as more vertices are identified.

        1. Seed

          1. Feasibility

            Successful retrieval of GF from GT is guaranteed if GF exhibits the following structural properties.

  • GF is uniquely identifiable, i.e., no subgraph H GT except GF is isomorphic to GF . For example, in Figure 2, sub graph {v1, v2, v3} is isomorphic to sub graph

    {v1, v4, v5} because there is a structure-preserving

    mapping v1 7 v1, v2 7 v4, v3 7 v5 between them. Therefore, the two sub graphs are structurally indistinguishable once the vertex labels are removed.

    if we could locate VF={v1,.v5}from GT, v2,..v5 are indistinguishable once their labels are even

    In practice, since the structure of other nodes in the network is unknown to the attacker before its release, the uniquely identifiable property is not realizable. How-ever, as was proved by Backstrom et al. [3], with a large enough size and randomly generated edges under the Erdos¨-Renyi´ model

    [24], GF will be uniquely identifiable with high probability.

    Although a randomly generated graph GF is very likely to

    be uniquely identifable in GT , it may violate the asymmetric structural property. However, because the goal of seed is to

    identify the initial seed VS rather than the fingerprint GF , the asymmetric requirement for GF can be relaxed. For u VS , let VF (u) be the vertices in VF which connect with u (|VF (u)|

    1 by the definition of VS ). For each pair of vertices, say u and v, in VS , as long as VF (u) and VF (v) are distinguishable in GF (e.g., |VF (u)| 6= |VF (v)| or the degree sequences are different; more precisely, no automorphism of GF exists which maps VF (u) to VF (v)), and once GF is recovered from GT , VS can be identified uniquely. In Figure 2, since VF (6) = {v2, v3} and VF (7) = {v4, v5} are not distinguishable, vertices v6 and v7 cannot be identified through GF .

    Based on these observations, we propose the following method of constructing and recovering GF .

        1. Construction

    The construction of GF starts with a star structure. The motivation for the star structure will become clear in Section

    3.1.3. We call the vertex at the center of the star the head of GF and denote it by vh. vh connects and only connects to every other vertex in GF .

    The vertices in VF {vh} are connected with some other

    vertices of the initial seeds VS in GT . To ensure the distinguish ability of two seeds u and v once the fingerprint

    GF is recovered, the attacker can decline those connection

    requests (from other vertices in GT ) which render VF (u) =

    VF (v). Note that the attacker is not assumed to have full control over the connections: an attacker does not have to impose a connection as long as he can decline it.

  • GF is asymmetric, i.e., GF does not have any non-

trivial automorphism. For example, in Figure 2, sub-graph

{v1, v2, . . . , v5} has an automorphism v1 7 v1, v2 7v3, v3 7v4, v4 7v5, v5 7v2. Therefore,

After setting up the initial star structure, the attacker

establishes other internal connections within the finger-print graph GF . Two principles dictate this process:

  1. No automorphism of GF should map VF (u) to VF (v) for two distinct initial seeds u and v.

  2. The constructed GF should leave no distinctive structural pattern for anyone besides the attacker, but should yet be recoverable.

Principle 1 follows from the discussion in Section 3.1.1: a pair of initial seeds u and v could be unambiguously identified only if

no automorphism of GF maps VF (u) to VF (v). Principle 2 apparently presents a dilemma: GF should mingle with the rest of

the target graph GT , yet be distinctive. In the following discussion, we first

justify this principle, and then resolve the dilemma by reconciling the two competing requirements.

The motivation for having GF mingle with the rest of the

target graph GT is to avoid leaving distinctive structural patterns for defenders. Otherwise, a straight-forward defense against the proposed attack would be to locate the fingerprint

graph GF by pattern-matching and to remove it prior to the publication of GT . An implication is that the construction of GF should be stochastic rather than deterministic.

Yet, stochastic construction alone is not enough for GF to

blend into GT . Numerous studies [25, 26, 27, 28, 29, 30, 31] indicate the existence of distinctive structural properties of online social networks as opposed to arbitrary random graphs. In particular, online social graphs consist of a well-connected backbone linking numerous small communities [25]. Within each community, vertices show a local, transitive, triangle-

closing connection pat-tern [29]. The construction of GF should reflect these properties to blend into GT.

The cost for the attacker to set up the fingerprint graph GF is dominated by the number and variety of connections

between VF and the initial seeds VS . To minimize the cost,

the construction of GF mimics a local com-munity in GT [25]: after establishing the star structure centering at the head vertex

vh, each pair of vertices in VF {vh} connects with a probability of t. The probability t reflects the transitivity of a

community in GT , which is the likelihood that, in the same community, two vertices sharing a common neighbor (vh in GF ) will connect to each other. In reality, the attacker almost

always knows some auxiliary information about the target graph GF , which may include the community transitivity and a reasonable size for a community: The construction of GF should be adjusted to such information for GF to blend into GT

After connecting pairs of non-head vertices in VF with a probability of the community transitivity t, the attacker

collects the internal degree DF (v), which is number of vertices in VF that v connects to, for every v VF {vh} into an ordered sequence SD.

Now, for every v VS , v has a corresponding subsequence SD(v) of SD according to its connectivity with VF . For example, in Figure 2, v6 connects to v2 and v3 from GF ; since DF (v2) = DF (v3) = 1, SD(v6) = p, 1i. As long as SD(u) 6=

SD(v) for u and v from VS , no automorphism of GF will map VF (u) to VF (v). There-fore, the attacker guarantees unambiguous recovery of VS by ensuring that the randomly connected GF satisfies this condition. If not, the attacker will simply redo the random connection among VF {vh} until it does (which it eventually will, since we assume that VF (u) 6=

VF (v) for any pair u and v from VS ). Algorithm 1 summarizes the procedure.[Seed construction.] Bob had created

7 accounts vh and v1, . . . , v6, i.e., VF . He first connected vh with v1, . . . , v6. After a while, he noticed that users

v7 to v10 are connected with v1, . . . , v6, i.e., VS =

{v7, . . . , v10}.

Fig. 3.2 The task of the seed stage is to identify the initial seed by recovering the fingerprint graph GF.

Then, he randomly connected v1, . . . , v6 with the

community transitivity t and got the resulting graph GF ,as shown in Figure 3. The ordered internal

degree sequence SD = p, 2, 2, 3, 3, 4i.Bob found out that SD(v7) = pi, SD(v8) = p, 2i, SD(v9) = p, 3, 4i, and SD(v10)

= p, 3i. Since they are mutually distinct, Bob was sure that he could

identify v7 to v10 once VF was recovered from the published anonymized graph.

3.2 Grow

The initial seeds VS provide a firm ground for further identification in the anonymized graph GT. Background knowledge GB comes into play at this stage.

We have a partial mapping between GT and GB , i.e., the

initial seeds VS in GT map to corresponding vertices in GB . Two examples of partial graph mappings are the Twitter and Flickr datasets [2] and the Netflix and IMDB datasets [32]. The straightforward idea of testing all possible mappings for the rest of the vertices has an exponential complexity, which is unacceptable even for a medium-sized network. Besides, the overlapping

Figure 3.3 shows a small example. v7 to v10 have already been identified in the seed stage (recall Figure 3). The task is to

identify other vertices in the target graph GT .

Figure 3.3 The task of the grow stage is to identify the unmapped vertices

starting from the seed.

based on the attackers existing knowledge of the users social relations. We identify and relax implicit assump-tions for unambiguous seed identification taken by pre-vious works, eliminate arbitrary parameters in grow algorithm, and demonstrate the superior performance over previous works in terms of identification effective-ness and accuracy by simulations on real-world-collected social-network datasets

between GT and GB may be partial, so a full mapping is neither possible nor necessarily desirable. Therefore, the grow algorithm adopts a progressive and self-reinforcing strategy, starting with the initial seeds and extending the mapping to other vertices for each round

IV.CONCLUSION

Seed-and-Grow, to identfy users from an anonymized social graph. Our algorithm exploits the increasing overlapping user-bases among services and is based solely on social graph structure. The algorithm first identifies a seed sub-graph, either planted by an attacker or divulged by collusion of a small group of users, and then grows the seed larger

REFERENCE

  1. B. Krishnamurthy and C. E. Wills, Characterizing privacy in online social networks, in Proc. ACM WOSN, 2008.

  2. A. Narayanan and V. Shmatikov, De-anonymizing social net-works, in Proc. IEEE S&P, 2009.

  3. L. Backstrom, C. Dwork, and J. Kleinberg, Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography, in Proc. ACM WWW, 2007.

  4. M. Hay, G. Makalu, D. Jensen, P. Weis, and S. Srivastava, Anonymizing social networks, Univ. Massachusetts, Amherst, Tech. Rep., 2007.

  5. E. Zheleva and L. Getoor, Preserving the privacy of sensitive

    relationships in graph data, in Proc. ACM SIGKDD, 2007.

  6. A. Korolova, R. Motwani, S. Nabar, and Y. Xu, Link privacy in social networks, in Proc. ACM CIKM, 2008.

  7. B. Zhou and J. Pei, Preserving privacy in social networks against neighborhood attacks, in Proc. Intl. Conf. on Data Engineering (ICDE). IEEE, 2008.

Leave a Reply

Your email address will not be published. Required fields are marked *