Items shared through Social Media may affect more than one user's privacy—e.g., photos that depict multiple users, comments that mention multiple users, events in which multiple users are invited, etc. The lack of multi-party privacy management support in current mainstream Social Media infrastructures makes users unable to appropriately control to whom these items are actually shared or not. Computational mechanisms that are able to merge the privacy preferences of multiple users into a single policy for an item can help solve this problem. However, merging multiple users’ privacy preferences is not an easy task, because privacy preferences may conflict, so methods to resolve conflicts are needed. Moreover, these methods need to consider how users’ would actually reach an agreement about a solution to the conflict in order to propose solutions that can be acceptable by all of the users affected by the item to be shared. Current approaches are either too demanding or only consider fixed ways of aggregating privacy preferences. In this paper, we propose the first computational mechanism to resolve conflicts for multi-party privacy management in Social Media that is able to adapt to different situations by modelling the concessions that users make to reach a solution to the conflicts. We also present results of a user study in which our proposed mechanism outperformed other existing approaches in terms of how many times each approach matched users’ behaviour.
In @cite_27 users must manually define for each item: the privacy settings for the item, their trust to the other users, the sensitivity of the item, and how much privacy risk they would like to take. These parameters are used to calculate what the authors call privacy risk and sharing loss on segments --- they define segments as the set of conflicting target users among a set of negotiating users. Then, based on these measures all of the conflicting target users in each segment are assigned the same action. That is, all of the conflicts that a set of negotiating users have would be solved either by granting or denying access. Clearly, not considering that each individual conflict can have a different solution leads to outcomes that are far from what the users would be willing to accept. Moreover, due to how the privacy risk and sharing loss metrics are defined, solutions are likely to be the actions preferred by the majority of negotiating users, which can be many times far from the actual behaviour of users as shown in Section .
@cite_27
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We initiate the study of markets for private data, through the lens of differential privacy. Although the purchase and sale of private data has already begun on a large scale, a theory of privacy as a commodity is missing. In this paper, we propose to build such a theory. Specifically, we consider a setting in which a data analyst wishes to buy information from a population from which he can estimate some statistic. The analyst wishes to obtain an accurate estimate cheaply, while the owners of the private data experience some cost for their loss of privacy, and must be compensated for this loss. Agents are selfish, and wish to maximize their profit, so our goal is to design truthful mechanisms. Our main result is that such problems can naturally be viewed and optimally solved as variants of multi-unit procurement auctions. Based on this result, we derive auctions which are optimal up to small constant factors for two natural settings: When the data analyst has a fixed accuracy goal, we show that an application of the classic Vickrey auction achieves the analyst's accuracy goal while minimizing his total payment. When the data analyst has a fixed budget, we give a mechanism which maximizes the accuracy of the resulting estimate while guaranteeing that the resulting sum payments do not exceed the analyst's budget. In both cases, our comparison class is the set of envy-free mechanisms, which correspond to the natural class of fixed-price mechanisms in our setting. In both of these results, we ignore the privacy cost due to possible correlations between an individual's private data and his valuation for privacy itself. We then show that generically, no individually rational mechanism can compensate individuals for the privacy loss incurred due to their reported valuations for privacy. This is nevertheless an important issue, and modeling it correctly is one of the many exciting directions for future work. Suppose that party A collects private information about its users, where each user's data is represented as a bit vector. Suppose that party B has a proprietary data mining algorithm that requires estimating the distance between users, such as clustering or nearest neighbors. We ask if it is possible for party A to publish some information about each user so that B can estimate the distance between users without being able to infer any private bit of a user. Our method involves projecting each user's representation into a random, lower-dimensional space via a sparse Johnson-Lindenstrauss transform and then adding Gaussian noise to each entry of the lower-dimensional representation. We show that the method preserves differential privacy---where the more privacy is desired, the larger the variance of the Gaussian noise. Further, we show how to approximate the true distances between users via only the lower-dimensional, perturbed data. Finally, we consider other perturbation methods such as randomized response and draw comparisons to sketch-based methods. While the goal of releasing user-specific data to third parties is more broad than preserving distances, this work shows that distance computations with privacy is an achievable goal. Abstract This study summarizes the development and validation of a multidimensional privacy orientation scale designed for measuring privacy attitudes of Social Network Site (SNS) users. Findings confirm the existence of four dimensions: (1) belief in the value of “privacy as a right”; (2) “other-contingent privacy”; (3) “concern about own informational privacy” and (4) “concern about privacy of others.” Moreover, a segmentation of SNS users based on these attitude scores reveals three types of users: (1) privacy advocates, who are concerned about both their own and other people’s privacy; (2) privacy individualists, who are concerned mostly about their own privacy, and (3) privacy indifferents, whose score on all dimensions are lower than other segments. The results indicate that the four privacy orientation dimensions and three user segments predict key differences in terms of privacy protective behavior, information disclosure, and viewing personal information of others. We present a 3-week user study in which we tracked the locations of 27 subjects and asked them to rate when, where, and with whom they would have been comfortable sharing their locations. The results of analysis conducted on over 7,500 h of data suggest that the user population represented by our subjects has rich location-privacy preferences, with a number of critical dimensions, including time of day, day of week, and location. We describe a methodology for quantifying the effects, in terms of accuracy and amount of information shared, of privacy-setting types with differing levels of complexity (e.g., setting types that allow users to specify location- and or time-based rules). Using the detailed preferences we collected, we identify the best possible policy (or collection of rules granting access to one's location) for each subject and privacy-setting type. We measure the accuracy with which the resulting policies are able to capture our subjects' preferences under a variety of assumptions about the sensitivity of the information and user-burden tolerance. One practical implication of our results is that today's location-sharing applications may have failed to gain much traction due to their limited privacy settings, as they appear to be ineffective at capturing the preferences revealed by our study.