594 research outputs found
Prevention of Cross-update Privacy Leaks on Android
Updating applications is an important mechanism to enhance their availability, functionality, and security. However, without careful considerations, application updates can bring other security problems. In this paper, we consider a novel attack that exploits application updates on Android: a cross-update privacy-leak attack called COUPLE. The COUPLE attack allows an application to secretly leak sensitive data through the cross-update interaction between its old and new versions; each version only has permissions and logic for either data collection or transmission to evade detection. We implement a runtime security system, BREAKUP, that prevents cross-update sensitive data transactions by tracking permission-use histories of individual applications. Evaluation results show that BREAKUPās time overhead is below 5%. We further show the feasibility of the COUPLE attack by analyzing the versions of 2,009 applications (28,682 APKs). Ā© 2018, ComSIS Consortium. All rights reserved.11Ysciescopu
Towards Automated Android App Collusion Detection
Android OS supports multiple communication methods between apps. This opens
the possibility to carry out threats in a collaborative fashion, c.f. the
Soundcomber example from 2011. In this paper we provide a concise definition of
collusion and report on a number of automated detection approaches, developed
in co-operation with Intel Security
ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs
The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve usersā location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., userās pro- file attributes or background knowledge of an attacker may reveal the userās identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering usersā profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal
Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values
Two-party secure function evaluation (SFE) has become significantly more
feasible, even on resource-constrained devices, because of advances in
server-aided computation systems. However, there are still bottlenecks,
particularly in the input validation stage of a computation. Moreover, SFE
research has not yet devoted sufficient attention to the important problem of
retaining state after a computation has been performed so that expensive
processing does not have to be repeated if a similar computation is done again.
This paper presents PartialGC, an SFE system that allows the reuse of encrypted
values generated during a garbled-circuit computation. We show that using
PartialGC can reduce computation time by as much as 96% and bandwidth by as
much as 98% in comparison with previous outsourcing schemes for secure
computation. We demonstrate the feasibility of our approach with two sets of
experiments, one in which the garbled circuit is evaluated on a mobile device
and one in which it is evaluated on a server. We also use PartialGC to build a
privacy-preserving "friend finder" application for Android. The reuse of
previous inputs to allow stateful evaluation represents a new way of looking at
SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the
2014 ACM SIGSAC Conference on Computer and Communications Security, Pages
582-596, ACM New York, NY, US
Integrated Framework for Data Quality and Security Evaluation on Mobile Devices
Data quality (DQ) is an important concept that is used in the design and employment of information, data management, decision making, and engineering systems with multiple applications already available for solving specific problems. Unfortunately, conventional approaches to DQ evaluation commonly do not pay enough attention or even ignore the security and privacy of the evaluated data. In this research, we develop a framework for the DQ evaluation of the sensor originated data acquired from smartphones, that incorporates security and privacy aspects into the DQ evaluation pipeline. The framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor data quality and security metrics. The framework employs a knowledge graph to facilitate its adaptation in new applications development and enables knowledge accumulation. Privacy aspects evaluation is demonstrated by the detection of novel and sophisticated attacks on data privacy on the example of colluded applications attack recognition. We develop multiple calculi for DQ and security evaluation, such as a hierarchical fuzzy rules expert system, neural networks, and an algebraic function. Case studies that demonstrate the framework\u27s performance in solving real-life tasks are presented, and the achieved results are analyzed. These case studies confirm the framework\u27s capability of performing comprehensive DQ evaluations. The framework development resulted in producing multiple products, and tools such as datasets and Android OS applications. The datasets include the knowledge base of sensors embedded in modern mobile devices and their quality analysis, technological signals recordings of smartphones during the normal usage, and attacks on users\u27 privacy. These datasets are made available for public use and can be used for future research in the field of data quality and security. We also released under an open-source license a set of Android OS tools that can be used for data quality and security evaluation
AdSplit: Separating smartphone advertising from applications
A wide variety of smartphone applications today rely on third-party
advertising services, which provide libraries that are linked into the hosting
application. This situation is undesirable for both the application author and
the advertiser. Advertising libraries require additional permissions, resulting
in additional permission requests to users. Likewise, a malicious application
could simulate the behavior of the advertising library, forging the user's
interaction and effectively stealing money from the advertiser. This paper
describes AdSplit, where we extended Android to allow an application and its
advertising to run as separate processes, under separate user-ids, eliminating
the need for applications to request permissions on behalf of their advertising
libraries.
We also leverage mechanisms from Quire to allow the remote server to validate
the authenticity of client-side behavior. In this paper, we quantify the degree
of permission bloat caused by advertising, with a study of thousands of
downloaded apps. AdSplit automatically recompiles apps to extract their ad
services, and we measure minimal runtime overhead. We also observe that most ad
libraries just embed an HTML widget within and describe how AdSplit can be
designed with this in mind to avoid any need for ads to have native code
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