Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Location Anonymization With Considering Errors and Existence Probability

Mobile devices that can sense their location using GPS or Wi-Fi have become extremely popular. However, many users hesitate to provide their accurate location information to unreliable third parties if it means that their identities or sensitive attribute values will be disclosed by doing so. Many approaches for anonymization, such as k-anonymity, have been proposed to tackle this issue. Existing studies for k-anonymity usually anonymize each user\u27s location so that the anonymized area contains k or more users. Existing studies, however, do not consider location errors and the probability that each user actually exists at the anonymized area. As a result, a specific user might be identified by untrusted third parties. We propose novel privacy and utility metrics that can treat the location and an efficient algorithm to anonymize the information associated with users\u27 locations. This is the first work that anonymizes location while considering location errors and the probability that each user is actually present at the anonymized area. By means of simulations, we have proven that our proposed method can reduce the risk of the user\u27s attributes being identified while maintaining the utility of the anonymized data

Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness

A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets

A look ahead approach to secure multi-party protocols

Secure multi-party protocols have been proposed to enable non-colluding parties to cooperate without a trusted server. Even though such protocols prevent information disclosure other than the objective function, they are quite costly in computation and communication. Therefore, the high overhead makes it necessary for parties to estimate the utility that can be achieved as a result of the protocol beforehand. In this paper, we propose a look ahead approach, specifically for secure multi-party protocols to achieve distributed k-anonymity, which helps parties to decide if the utility benefit from the protocol is within an acceptable range before initiating the protocol. Look ahead operation is highly localized and its accuracy depends on the amount of information the parties are willing to share. Experimental results show the effectiveness of the proposed methods