1 / 19

May 20 Offense

To Join or Not join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles. May 20 Offense. General Issues. Propose 8 attack methods Tell and give people a ‘knife’ to attack leak points. Do not propose the solutions to the attacks

pandora-sims
Download Presentation

May 20 Offense

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. To Join or Not join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles May 20 Offense

  2. General Issues • Propose 8 attack methods Tell and give people a ‘knife’ to attack leak points • Do not propose the solutions to the attacks If attacks happened before the company’s innovation, maybe the paper should be partly responsible for the attack.

  3. Research foundation • membership=public the foundation of all researches • if membership=private for example, if facebook allows group membership to be private, all researches depending on membership are impossible.

  4. Short-life research • The prerequisites of research may disappear at any moment ,the future work is not necessary to develop. • Especially, the results of the prediction research are not good. so, what is the value of the research?

  5. Unadvisable dataset choice So much blank and none options, so much small number sample, what the paper want to tell us? Further, maybe the author want to demonstrate the research results, but what are they?

  6. 4.1 Attacks without links and groups • BASIC “this attack is always at least as good asrandom guess” A serious scientific problem, the results are approximately equal to random guess!

  7. 4.2 Privacy attacks using links AGG,CC, LINK,BLOCK The four methods didn’t present good successful attack results; especially, in DOGSTER and BiBSONOMY, the link analysis is not useful.

  8. 4.2 Privacy attacks using links The paper frequently uses link relationship to describe and solve problem, in fact, the methods using links is unsuccessful, at last, the paper discards this attack category. As the authors recognize themselves, “Surprisingly, link-based methods did not perform as well as we expected “ what is the research value using link?

  9. 4.3.1 Clique Groupmate-link model is ,in fact, link-based model. And the paper assumes each clique is a friendship link between users, in fact, it is only a assume. What is the new point proposed in this model?

  10. 4.3.2 Group-based “ if entropy of group is low, the group is selected successfully” What is the threshold which divides the low and the high entropy? there is no discussion on how to determine the value of threshold.

  11. 5.1 Data description “Although BibSonomy allows users to form friendships and join groups of interest, the dataset did not contain this information” People did not prefer to building link in the real-world social network, the paper creates group artificially in order to use the group-based method to analyze, why?

  12. 5.2 Experimental “we split the data into test and training by randomly…” Are the results consistent by splitting data randomly multiple times? The paper should describe the dataset which the result depends on, randomly? average? or the best one?

  13. 5.3.1 Flickr Even the paper recommends the high successful attacks ：LINK-GROUP(“This model uses all links and groups as features, thus utilizing the full power of available information”)is not good enough. “Link-Group, did not perform statistically different from the Group model, this showed that adding the links to the GROUP model did not lead to an additional benefit.” What on earth does the paper tell to reader?

  14. 5.3.1 Flickr Node coverage =0.02%,the size of node is not meaningful for research, and of course, the result should be 100%. why display the option?

  15. 5.3.2 Facebook • “we performed the same experiment for Facebook but we omit the figure due to space constraints” Yes, the paper does not have space to present a result graph( why?!), but has enough space to tell us: ……

  16. 5.3.2 Facebook “…..very liberal orliberal(545),moderate(210),conservative or veryconservative(114),libertarian(29),apathetic(18),and other(49)”…..” “There are mostly toy dogs(749),the other categories were working(268),herding(202),terrier(232),sporting(308),non-sporting(225),hound(152) and mixeddogs(506)…….” The number and categories are not used in any other place in the paper. Who care about the details on categories? why the paper describe them detailedly?

  17. Prediction, hard work Crawl data, graph or build link, select group, training classifier…..hard work ….but results is not good enough to predict. What is the aim? Predict privacy profile! Why don’t you try to build a real friend link to users, you can get the whole and accurate,100%privacy profile if you are one of their friends.

  18. . Facebook Social network Complex profile Friend link application response Friend link application Simple profile response The two categories application got almost same number responses after one week, a lot of people don’t care the details of friend requests, and agree to create friend links easily.

  19. 7 Discussion “if a marketing company can predict the gender and location of users with hidden profiles, it can improve its targeted marking” What prediction system would company pay?

More Related