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Register a new userUnderstanding Worldwide Private Information Collection on Android
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Added by
Gianluca Stringhini
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a users location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest study of private information collection (PIC) on Android to date. Leveraging an anonymized dataset collected from the customers of a popular mobile security product, we analyze the flows of sensitive information generated by 2.1M unique apps installed by 17.3M users over a period of 21 months between 2018 and 2019. We find that 87.2% of all devices send private information to at least five different domains, and that actors active in different regions (e.g., Asia compared to Europe) are interested in collecting different types of information. The United States (62% of the total) and China (7% of total flows) are the countries that collect most private information. Our findings raise issues regarding data regulation, and would encourage policymakers to further regulate how private information is used by and shared among the companies and how accountability can be truly guaranteed.
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Androids graphical password unlock remains one of the most widely used schemes for phone unlock authentication, and it is has been studied extensively in the last decade since its launch. We have learned that users choice of patterns mimics the poor password choices in other systems, such as PIN or text-based passwords. A wide variety of analysis and data collections methods was used to reach these conclusions, but what is missing from the literature is a systemized comparison of the related work in this space that compares both the methodology and the results. In this paper, we take a detailed accounting of the different methods applied to data collection and analysis for Android unlock patterns. We do so in two dimensions. First we systemize prior work into a detailed taxonomy of collection methods, and in the second dimension, we perform a detailed analysis of 9 different data sets collected using different methods. While this study focuses singularly on the collection methods and comparisons of the Android pattern unlock scheme, we believe that many of the findings generalize to other graphical password schemes, unlock authentication technology, and other knowledge-based authentication schemes.
Data collection under local differential privacy (LDP) has been mostly studied for homogeneous data. Real-world applications often involve a mixture of different data types such as key-value pairs, where the frequency of keys and mean of values under each key must be estimated simultaneously. For key-value data collection with LDP, it is challenging to achieve a good utility-privacy tradeoff since the data contains two dimensions and a user may possess multiple key-value pairs. There is also an inherent correlation between key and values which if not harnessed, will lead to poor utility. In this paper, we propose a locally differentially private key-value data collection framework that utilizes correlated perturbations to enhance utility. We instantiate our framework by two protocols PCKV-UE (based on Unary Encoding) and PCKV-GRR (based on Generalized Randomized Response), where we design an advanced Padding-and-Sampling mechanism and an improved mean estimator which is non-interactive. Due to our correlated key and value perturbation mechanisms, the composed privacy budget is shown to be less than that of independent perturbation of key and value, which enables us to further optimize the perturbation parameters via budget allocation. Experimental results on both synthetic and real-world datasets show that our proposed protocols achieve better utility for both frequency and mean estimations under the same LDP guarantees than state-of-the-art mechanisms.
Anonymous data collection systems allow users to contribute the data necessary to build services and applications while preserving their privacy. Anonymity, however, can be abused by malicious agents aiming to subvert or to sabotage the data collection, for instance by injecting fabricated data. In this paper we propose an efficient mechanism to rate-limit an attacker without compromising the privacy and anonymity of the users contributing data. The proposed system builds on top of Direct Anonymous Attestation, a proven cryptographic primitive. We describe how a set of rate-limiting rules can be formalized to define a normative space in which messages sent by an attacker can be linked, and consequently, dropped. We present all components needed to build and deploy such protection on existing data collection systems with little overhead. Empirical evaluation yields performance up to 125 and 140 messages per second for senders and the collector respectively on nominal hardware. Latency of communication is bound to 4 seconds in the 95th percentile when using Tor as network layer.
Searching for available parking spaces is a major problem for drivers especially in big crowded cities, causing traffic congestion and air pollution, and wasting drivers time. Smart parking systems are a novel solution to enable drivers to have real-time parking information for pre-booking. However, current smart parking requires drivers to disclose their private information, such as desired destinations. Moreover, the existing schemes are centralized and vulnerable to the bottleneck of the single point of failure and data breaches. In this paper, we propose a distributed privacy-preserving smart parking system using blockchain. A consortium blockchain created by different parking lot owners to ensure security, transparency, and availability is proposed to store their parking offers on the blockchain. To preserve drivers location privacy, we adopt a private information retrieval (PIR) technique to enable drivers to retrieve parking offers from blockchain nodes privately, without revealing which parking offers are retrieved. Furthermore, a short randomizable signature is used to enable drivers to reserve available parking slots in an anonymous manner. Besides, we introduce an anonymous payment system that cannot link drivers to specific parking locations. Finally, our performance evaluations demonstrate that the proposed scheme can preserve drivers privacy with low communication and computation overhead.
We consider user-private information retrieval (UPIR), an interesting alternative to private information retrieval (PIR) introduced by Domingo-Ferrer et al. In UPIR, the database knows which records have been retrieved, but does not know the identity of the query issuer. The goal of UPIR is to disguise user profiles from the database. Domingo-Ferrer et al. focus on using a peer-to-peer community to construct a UPIR scheme, which we term P2P UPIR. In this paper, we establish a strengthened model for P2P UPIR and clarify the privacy goals of such schemes using standard terminology from the field of privacy research. In particular, we argue that any solution providing privacy against the database should attempt to minimize any corresponding loss of privacy against other users. We give an analysis of existing schemes, including a new attack by the database. Finally, we introduce and analyze two new protocols. Whereas previous work focuses on a special type of combinatorial design known as a configuration, our protocols make use of more general designs. This allows for flexibility in protocol set-up, allowing for a choice between having a dynamic scheme (in which users are permitted to enter and leave the system), or providing increased privacy against other users.
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