De-anonymizing D4D Datasets
نویسندگان
چکیده
Recent research on de-anonymizing datasets of anonymized personal records has not deterred organizations from releasing personal data, often with ingenuous attempts at defeating de-anonymization. Studying such techniques provides scientific evidence as to why anonymization of high dimensional databases is hard and throws light on what kinds of techniques to avoid. We study how to de-anonymize datasets released as a part of Data for Development (D4D) challenge [12]. We show that the anonymization strategy used is weak and allows an attacker to re-identify and link records efficiently, we also suggest some measures to make such
منابع مشابه
D4D-Senegal: The Second Mobile Phone Data for Development Challenge
The D4D-Senegal challenge is an open innovation data challenge on anonymous call patterns of Orange’s mobile phone users in Senegal. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Senegalese population. Participants to the challenge are given access to three mobile phone datasets. T...
متن کاملAnonymizing Unstructured Data
In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. W...
متن کاملData for Development: the D4D Challenge on Mobile Phone Data
The Orange “Data for Development” (D4D) challenge is an open data challenge on anonymous call patterns of Orange’s mobile phone users in Ivory Coast. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Ivory Coast population. Participants to the challenge are given access to four mobile ...
متن کاملDe-anonymizing social networks
The problem of de-anonymizing social networks is to identify the same users between two anonymized social networks [7] (Figure 1). Network de-anonymization task is of multifold significance, with user profile enrichment as one of its most promising applications. After the deanonymization and alignment, we can aggregate and enrich user profile information from different online networking service...
متن کاملDisclosure Risk Measurement of Anonymized Datasets after Probabilistic Attacks
We present a unified metric for analyzing the risk of disclosing anonymized datasets. Datasets containing privacy sensitive information are often required to be shared with unauthorized users for utilization of valuable statistical properties of the data. Anonymizing the actual data provides a great opportunity to share the data while preserving its statistical properties and privacy. The risk ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013