m-Privacy for Collaborative Data Publishing

Abstract

Abstract-In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of "insider attack" by colluding data providers who may use their own data records (a subset of the overall data) in addition to the external background knowledge to infer the data records contributed by other data providers. The paper addresses this new threat and makes several contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic algorithms exploiting the equivalence group monotonicity of privacy constraints and adaptive ordering techniques for efficiently checking m-privacy given a set of records. Finally, we present a data provider-aware anonymization algorithm with adaptive mprivacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency. Experiments on real-life datasets suggest that our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while providing m-privacy guarantee.

Data Privacy Protection Mechanisms in Cloud

“…Specifically, most of the existing techniques assess the performance using traditional metrics only. Unlike those, the information loss is measured in terms of traditional metrics such as global certainty penalty [4,24], non-uniform entropy metric [25], normalized information loss [26,27], normalized certainty penalty [4], query error [11,28], sum of squared errors [29]. But, in proposed approach, Nayahi and Kavitha worked best to succeed in the anonymization using centroid-based replacement of QID values that is computationally superior to suppression in terms of information loss and less expensive than generalization.…”

Data attribute security and privacy in distributed database system

“…L diversity helps to overcome this problem. In current research paper [1], authors introduce a m privacy algorithm which verify anonymization and L diversity. For this they consider generalization and bucketization techniques for maintaining anonimized view of data and also provide L diversity which help to increase privacy of data.…”

DObjects+: Enabling Privacy-Preserving Data Federation Services

“…We propose to demonstrate DObjects+, a scalable and extensible framework that is aimed to enable privacy preserving data federation services. The framework extends our DObjects architecture [6], [8] with our ongoing work on distributed anonymization protocols [7], [5], [18] and secure query processing protocols [9] for a seamless access to distributed and possibly private data. We summarize the contributions of the demonstrated framework below.…”

“…While the framework is orthogonal to different privacy principles, we studied several representative state-ofthe-art privacy principles within our framework including ldiversity [14], t-closeness [12], and differential privacy [3], [10]. We show the implications of adopting them in the distributed setting with respect to the above attack space and integrated new or modified notions and algorithms in our framework [7], [5], [18].…”

“…It becomes worse when data providers collude with each other. We propose a general distributed adversarial privacy notion, m-privacy, which specifically models an attacker consisting of up to m colluding providers [5]. An anonymization satisfies m-privacy with respect to a given privacy constraint C such as l-diversity, if and only if the records in each group excluding any subset of records from any m data providers satisfies the constraint C. Essentially, this definition considers any combination of up to m colluding providers as an m-adversary, and guarantees that the records in each group excluding any of those from the attacker still satisfies C. Our framework currently uses a greedy partitioning approach and an efficient algorithm for checking the constraints for all possible combinations of madversaries.…”