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Multi-Modality Mobile datasets for Behavioral Biometrics Research data/Toolset paper 23

Multi-Modality Mobile Datasets for Behavioral Biometrics Res...

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13th acm conference on data and application security and privacy (CODASPY)

作者： Ray-Dowling, Aratrika Wahab, Ahmed Anu Hou, Daqing Schuckers, Stephanie Clarkson Univ Dept Elect & Comp Engn Potsdam NY 13676 USA

ISBN: (纸本)9798400700675

The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities.

关键词： Mobile datasets Continuous Authentication Multi-Modalities Fusion Behavioral Biometrics

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A privacy-Preserving Smart Contract Vulnerability Detection Framework for Permissioned Blockchain 30

A Privacy-Preserving Smart Contract Vulnerability Detection ...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Tian, Wensheng Zhang, Lei Chen, Shuangxi Wang, Hu Luo, Xiao Nanhu Lab Jiaxing Peoples R China Jiaxing Vocat & Tech Coll Jiaxing Peoples R China Zhejiang Big Data Dev Adm Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798400700507

The two main types of blockchains that are currently widely deployed are public blockchains and permissioned blockchains. The research that has been conducted for blockchain vulnerability detection is mainly oriented to public blockchains. Less consideration is given to the unique requirements of the permissioned blockchains, which cannot be directly migrated to the application scenarios of the permissioned blockchains. The permissioned blockchain is deployed between verified organizations, and its smart contracts may contain sensitive information such as the transaction flow of the contracts, transaction algorithms. The sensitive information can be considered as the private information of the smart contracts themselves, which should be kept confidential to users outside the blockchain. In this paper, a privacy-preserving smart contract vulnerability detection framework is proposed. The framework leverages blockchain and confidential computing technologies to enable vulnerability detection in permissioned blockchain smart contracts while protecting the privacy of smart contracts. The framework is also able to protect the interests of vulnerability detection model owners. We experimentally validate the detection performance of our framework in a confidential computing environment.

关键词： Smart Contract privacy-preserving Vulnerability Detection Blockchain

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Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2 23

Attribute Inference Attacks in Online Multiplayer Video Game...

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13th acm conference on data and application security and privacy (CODASPY)

作者： Tricomi, Pier Paolo Apruzzese, Giovanni Facciolo, Lisa Conti, Mauro Univ Padua Dept Math Padua Italy Univ Liechtenstein Hilti Chair Data & Applicat Secur Vaduz Liechtenstein Delft Univ Technol Fac EEMCS Delft Netherlands

ISBN: (纸本)9798400700675

Did you know that over 70 million of DOTA2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the DOTA2 context. We explain how (and why) attackers can exploit the abundant public data in the DOTA2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on similar to 500 DOTA2 players spanning over 26k matches, we verify whether a correlation exists between a player's DOTA2 activity and their real-life. Then, after finding such a link (p < 0.01 and rho > 0.3), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applying domain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of DOTA2.

关键词： Attribute Inference Attack Video Games Dota2 Machine Learning

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MK-RS-PKE: Multi-Keyword Ranked Searchable Public-Key Encryption for Cloud-Assisted Lightweight Platforms 22

MK-RS-PKE: Multi-Keyword Ranked Searchable Public-Key Encryp...

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acm conference on data and application security and privacy (CODASPY)

作者： Mouri, Israt Jahan Ridowan, Muhammad Adnan, Muhammad Abdullah Bangladesh Univ Engn & Technol BUET Dhaka Bangladesh

ISBN: (纸本)9781450392204

Since more and more data from lightweight platforms like IoT devices or mobile apps are being outsourced to the cloud, the need to ensure privacy while retaining data usability is essential. This paper designs a framework where lightweight platforms like IoT devices can encrypt documents and generate document indexes using the public key before uploading the document to the cloud. An admin can search and retrieve the top-k most relevant documents that match a multi-keyword query using the private key. Most existing searchable encryption that supports IoT returns all the documents matching queried keywords. However, IoT devices can produce massive data, which is not practical for such schemes. We formally name our framework Multi-keyword Ranked Searchable Public-Key Encryption (MK-RS-PKE).

关键词： Forward privacy Dynamic PKE Keyword privacy Cloud security Multi-Keyword Ranked Searchable Encryption Lightweight Platforms IoT

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privacy - From the Ivory Tower to the Trenches in the Parliament 25

Privacy - From the Ivory Tower to the Trenches in the Parlia...

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proceedings of the fifteenth acm conference on data and application security and privacy

作者： Orr Dunkelman Computer Science Dept. University of Haifa Haifa Israel and Security in Telecommunications (SecT) Technische Universität Berlin Berlin Germany

ISBN: (纸本)9798400714764

privacy is a fundamental human right, but it is also one of the more complicated rights. Not only privacy is very context-oriented and society-oriented, it is hard to define privacy without involving adversaries trying to break it. The conceived tension between ''national security'' or ''public order'' and the basic human right of privacy, impacts the adoption of privacy Enhancing Technologies (PETs), and at times, hinders the ability to offer privacy to *** this talk, we will discuss a few topics where such a conflict exists, such as biometric data, contact tracing, and COVID certificates. We will discuss the importance of incorporating privacy into our technical work, thus making a real-life impact by offering solutions that offer better privacy. We will discuss the problems in disseminating state-of-the-art scientific knowledge to the *** will then consider the problem of discussing these issues with policy makers, as they are the ones making the call, while not necessarily understanding the impact of complicated technologies for (and on) privacy.

关键词： biometrics

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Landmark privacy: Configurable Differential privacy Protection for Time Series 22

Landmark Privacy: Configurable Differential Privacy Protecti...

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acm conference on data and application security and privacy (CODASPY)

作者： Katsomallos, Manos Tzompanaki, Katerina Kotzinos, Dimitris CY Cergy Paris Univ ETIS UMR 8051 ENSEA CNRS Paris France

ISBN: (纸本)9781450392204

Several application domains, including healthcare, smart building, and traffic monitoring, require the continuous publishing of data, also known as time series. In many cases, time series are geotagged data containing sensitive personal details, and thus their processing entails privacy concerns. Several definitions have been proposed that allow for privacy preservation while processing and publishing such data, with differential privacy being the most prominent one. Most existing differential privacy schemes protect either a single timestamp (event-level), or all the data per user (user-level), or per window (w-event-level) in the time series, considering however all timestamps as equally significant. In this work, we define a novel configurable privacy notion, landmark privacy, which differentiates events into significant (landmarks) and regular, achieving to provide better data utility while preserving adequately the privacy of each event. We propose three schemes that guarantee landmark privacy, and design an appropriate dummy landmark selection module to better protect the actual temporal position of the landmarks. Finally, we provide a thorough experimental study where (i) we study the behavior of our framework on real and synthetic data, with and without temporal correlation, and (ii) demonstrate that landmark privacy achieves generally better data utility in the presence of landmarks than user-level privacy.

关键词： differential privacy privacy-preserving data publishing time series

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Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential privacy with Optimal Gaussian Noise and application to Deep Learning 23

Geometry of Sensitivity: Twice Sampling and Hybrid Clipping ...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Xiao, Hanshen Wan, Jun Devadas, Srinivas MIT Cambridge MA 02139 USA

ISBN: (纸本)9798400700507

We study the fundamental problem of the construction of optimal randomization in Differential privacy (DP). Depending on the clipping strategy or additional properties of the processing function, the corresponding sensitivity set theoretically determines the necessary randomization to produce the required security parameters. Towards the optimal utility-privacy tradeoff, finding the minimal perturbation for properly-selected sensitivity sets stands as a central problem in DP research. In practice, l(2)/l(1)-norm clippings with Gaussian/Laplace noise mechanisms are among the most common setups. However, they also suffer from the curse of dimensionality. For more generic clipping strategies, the understanding of the optimal noise for a high-dimensional sensitivity set remains limited. This raises challenges in mitigating the worst-case dimension dependence in privacy-preserving randomization, especially for deep learning applications. In this paper, we revisit the geometry of high-dimensional sensitivity sets and present a series of results to characterize the non-asymptotically optimal Gaussian noise for Renyi DP (RDP). Our results are both negative and positive: on one hand, we show the curse of dimensionality is tight for a broad class of sensitivity sets satisfying certain symmetry properties;but if, fortunately, the representation of the sensitivity set is asymmetric on some group of orthogonal bases, we show the optimal noise bounds need not be explicitly dependent on either dimension or rank. We also revisit sampling in the high-dimensional scenario, which is the key for both privacy amplification and computation efficiency in large-scale data processing. We propose a novel method, termed twice sampling, which implements both sample-wise and coordinate-wise sampling, to enable Gaussian noises to fit the sensitivity geometry more closely. With closed-form RDP analysis, we prove twice sampling produces asymptotic improvement of the privacy amplification given an

关键词： Differential privacy Sensitivity Geometry Renyi DP Clipping Twice Sampling privacy Amplification Deep Learning

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Local Methods for privacy Protection and Impact on Fairness 23

Local Methods for Privacy Protection and Impact on Fairness

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13th acm conference on data and application security and privacy (CODASPY)

作者： Palamidessi, Catuscia Inria Saclay Palaiseau France Inst Polytech Paris Palaiseau France

ISBN: (纸本)9798400700675

The increasingly pervasive use of big data and machine learning is raising various ethical issues, in particular privacy and fairness. In this talk, I will discuss some frameworks to understand and mitigate the issues, focusing on iterative methods coming from information theory and statistics. In the area of privacy protection, differential privacy (DP) and its variants are the most successful approaches to date. One of the fundamental issues of DP is how to reconcile the loss of information that it implies with the need to preserve the utility of the data. In this regard, a useful tool to recover utility is the iterative Bayesian update (IBU), an instance of the expectation-maximization method from statistics. I will show that the IBU, combined with a version of DP called d-privacy (also known as metric differential privacy), outperforms the state-of-the-art, which is based on algebraic methods combined with the randomized response mechanism, widely adopted by the Big Tech industry (Google, Apple, Amazon, ... ). Then, I will discuss the issue of biased predictions in machine learning, and how DP can affect the level of fairness and accuracy of the trained model. Finally, I will show that the IBU can be applied also in this domain to ensure fairer treatment of disadvantaged groups and reconcile fairness and accuracy.

关键词： Differential privacy metric differential privacy iterative Bayesian update fairness

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Towards Fast and Scalable Private Inference 23

Towards Fast and Scalable Private Inference

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20th acm International conference on Computing Frontiers (CF)

作者： Mo, Jianqiao Garimella, Karthik Neda, Negar Ebel, Austin Reagen, Brandon NYU New York NY 10012 USA

ISBN: (纸本)9798400701405

privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for security fall short: they secure data when stored or communicated but must decrypt it for computation. Fortunately, a new paradigm of computing exists, which we refer to as privacy-preserving computation (PPC). Emerging PPC technologies can be leveraged for secure outsourced computation or to enable two parties to compute without revealing either users' secret data. Despite their phenomenal potential to revolutionize user protection in the digital age, the realization has been limited due to exorbitant computational, communication, and storage overheads. This paper reviews recent efforts on addressing various PPC overheads using private inference (PI) in neural network as a motivating application. First, the problem and various technologies, including homomorphic encryption (HE), secret sharing (SS), garbled circuits (GCs), and oblivious transfer (OT), are introduced. Next, a characterization of their overheads when used to implement PI is covered. The characterization motivates the need for both GCs and HE accelerators. Then two solutions are presented: HAAC for accelerating GCs and RPU for accelerating HE. To conclude, results and effects are shown with a discussion on what future work is needed to overcome the remaining overheads of PI.

关键词： private inference privacy preserving computation homomorphic encryption garbled circuits

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Are We Getting Well-informed? An In-depth Study of Runtime privacy Notice Practice in Mobile Apps 24

Are We Getting Well-informed? An In-depth Study of Runtime P...

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31st conference on Computer and Communications security

作者： Li, Shuai Yang, Zhemin Nan, Yuhong Yu, Shutian Zhu, Qirui Yang, Min Fudan Univ Shanghai Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9798400706363

Under the General data Protection Regulation (GDPR), mobile app developers are required to inform users of necessary information at the time when user data is collected (called users' "Right-to-be-Informed"). This is typically done by app developers via providing runtime privacy notices (RPNs for short). However, given the heterogeneous privacy data types and data access patterns in modern apps, it is not clear to what extent apps (app developers) effectively fulfill this compliance requirement in practice. In this paper, we perform the first systematic study of current RPN practices in mobile apps. Our research endeavors to comprehend (1) the ecosystem of RPN, (2) potential gaps between legal requirements and RPN practices, and (3) the underlying reasons for such gaps. To achieve this, we design an automated pipeline - RENO that can effectively identify, extract, and analyze RPN at a large scale. With the help of RENO, we investigated 4,656 mobile apps selected from 19 European Union countries. Our analysis reveals a number of interesting findings. For example, 77.10% of user data collection behaviors lack RPNs. Among those provided RPNs, 86.35% of them have no more than three required notice elements when GDPR requires seven. In addition, to further understand the reasons behind such gaps, we perform a notification campaign and ask for feedback from the app developers. Indeed, the collected responses highlighted several critical reasons. For instance, a substantial proportion of app developers regard RPN as an optional complement to their privacy policies as RPNs are not strictly enforced by app stores. Our study shows the pressing need for better transparency in user data collection delivered by RPN.

关键词： Runtime privacy Notice Right to be Informed Mobile application GDPR Compliance

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