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AuthPDB: Authentication of Probabilistic Queries on Outsourced Uncertain data 20

AuthPDB: Authentication of Probabilistic Queries on Outsourc...

引用

10th acm conference on data and application security and privacy (CODASPY)

作者： Zhang, Bo Dong, Boxiang Sun, Haipei Wang, Wendy Hui Stevens Inst Technol Hoboken NJ 07030 USA Montclair State Univ Montclair NJ USA

ISBN: (纸本)9781450371070

Query processing over uncertain data has gained much attention recently. Due to the high computational complexity of query evaluation on uncertain data, the data owner can outsource her data to a server that provides query evaluation as a service. However, a dishonest server may return cheap (and incorrect) query answers, hoping that the client who has weak computational power cannot catch the incorrect results. To address the integrity issue, in this paper, we design AuthPDB, a framework that supports efficient authentication of query evaluation for both all-answer and top-k queries on outsourced probabilistic databases. Our empirical results on real-world datasets demonstrate the effectiveness and efficiency of AuthPDB.

关键词： data security data outsourcing integrity verification probabilistic database

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IoT Expunge: Implementing Verifiable Retention of IoT data 20

IoT Expunge: Implementing Verifiable Retention of IoT Data

引用

10th acm conference on data and application security and privacy (CODASPY)

作者： Panwar, Nisha Sharma, Shantanu Gupta, Peeyush Ghosh, Dhrubajyoti Mehrotra, Sharad Venkatasubramanian, Nalini Augusta Univ Augusta GA 30912 USA Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9781450371070

The growing deployment of Internet of Things (IoT) systems aims to ease the daily life of end-users by providing several value-added services. However, IoT systems may capture and store sensitive, personal data about individuals in the cloud, thereby jeopardizing user-privacy. Emerging legislation, such as California's CalOPPA and GDPR in Europe, support strong privacy laws to protect an individual's data in the cloud. One such law relates to strict enforcement of data retention policies. This paper proposes a framework, entitled IoT Expunge that allows sensor data providers to store the data in cloud platforms that will ensure enforcement of retention policies. Additionally, the cloud provider produces verifiable proofs of its adherence to the retention policies. Experimental results on a real-world smart building testbed show that IoT Expunge imposes minimal overheads to the user to verify the data against data retention policies.

关键词： Internet of Things smart building user privacy data deletion verification

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Protecting DeFi Platforms against Non-Price Flash Loan Attacks 25

Protecting DeFi Platforms against Non-Price Flash Loan Attac...

引用

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

作者： Abdulrahman Alhaidari Balaji Palanisamy Prashant Krishnamurthy Informatics and Networked Systems University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9798400714764

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding 106 billion. These attacks use the atomicity property of blockchains to drain funds from smart contracts in a single transaction. While existing research primarily focuses on price manipulation attacks, such as oracle manipulation, mitigating non-price flash loan attacks that often exploit smart contracts' zero-day vulnerabilities remains largely unaddressed. These attacks are challenging to detect because of their unique patterns, time sensitivity, and complexity. In this paper, we present FlashGuard, a runtime detection and mitigation method for non-price flash loan attacks. Our approach targets smart contract function signatures to identify attacks in real-time and counterattack by disrupting the attack transaction atomicity by leveraging the short window when transactions are visible in the mempool but not yet confirmed. When FlashGuard detects an attack, it dispatches a stealthy dusting counterattack transaction to miners to change the victim contract's state which disrupts the attack's atomicity and forces the attack transaction to revert. We evaluate our approach using 20 historical attacks and several unseen attacks. FlashGuard achieves an average real-time detection latency of 150.31ms, a detection accuracy of over 99.93%, and an average disruption time of 410.92ms. FlashGuard could have potentially rescued over \405.71 million in losses if it were deployed prior to these attack instances. FlashGuard demonstrates significant potential as a DeFi security solution to mitigate and handle rising threats of non-price flash loan attacks.

关键词： DeFi security

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A Baseline for Attribute Disclosure Risk in Synthetic data 20

A Baseline for Attribute Disclosure Risk in Synthetic Data

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10th acm conference on data and application security and privacy (CODASPY)

作者： Hittmeir, Markus Mayer, Rudolf Ekelhart, Andreas SBA Res Vienna Austria

ISBN: (纸本)9781450371070

The generation of synthetic data is widely considered as viable method for alleviating privacy concerns and for reducing identification and attribute disclosure risk in micro-data. The records in a synthetic dataset are artificially created and thus do not directly relate to individuals in the original data in terms of a 1-to-1 correspondence. As a result, inferences about said individuals appear to be infeasible and, simultaneously, the utility of the data may be kept at a high level. In this paper, we challenge this belief by interpreting the standard attacker model for attribute disclosure as classification problem. We show how disclosure risk measures presented in recent publications may be compared to or even be reformulated as machine learning classification models. Our overall goal is to empirically analyze attribute disclosure risk in synthetic data and to discuss its close relationship to data utility. Moreover, we improve the baseline for attribute disclosure risk from the attacker's perspective by applying variants of the RadiusNearestNeighbor and the EnsembleVote classifiers.

关键词： privacy-Preserving data Mining Synthetic data Machine Learning

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CREPE: A privacy-Enhanced Crash Reporting System 20

CREPE: A Privacy-Enhanced Crash Reporting System

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10th acm conference on data and application security and privacy (CODASPY)

作者： Satvat, Kiavash Shirvanian, Maliheh Hosseini, Mahshid Saxena, Nitesh Univ Illinois Chicago IL 60680 USA Visa Res Foster City CA USA Univ Alabama Birmingham Birmingham AL USA

ISBN: (纸本)9781450371070

Software crashes are nearly impossible to avoid. The reported crashes often contain useful information assisting developers in finding the root cause of the crash. However, crash reports may carry sensitive and private information about the users and their systems, which may be used by an attacker who has compromised the crash reporting system to violate the user's privacy and security. Besides, a single bug may trigger loads of identical reports which excessively consumes system resources and overwhelms application developers. In this paper, we introduce CREPE, a security-concerned crash reporting solution, that effectively reduces the number of submitted crash reports to mitigate the security and privacy risk associated with the current implementation of the crash reporting system. Similar to the currently deployed systems, CREPE aggregates and categorizes the crashes based on their root cause. On top of that, the server marks the crash categories in which sufficient reports have been received as "saturated" and informs the clients periodically through software updates. On the client, CREPE engages the reporting application in categorizing each crash to only submit reports belonging to non-saturated categories. We evaluate CREPE using one year of data from Mozilla crash reporting system containing 38,834,383 reports of Firefox crashes. Our analysis suggests that we can significantly reduce the number of submitted reports by bucketing 100 most frequent crash signatures at the client. This helps to preserve the security and the privacy of a significant portion of users whose data has not been shared with the server due to the redundancy of their crash data with previously submitted reports.

关键词： Crash Reporting Crash Report privacy Crash Report security Browser privacy

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Renyi Differentially Private ADMM for Non-Smooth Regularized Optimization 20

Renyi Differentially Private ADMM for Non-Smooth Regularized...

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10th acm conference on data and application security and privacy (CODASPY)

作者： Chen, Chen Lee, Jaewoo Univ Georgia Athens GA 30602 USA

ISBN: (纸本)9781450371070

In this paper we consider the problem of minimizing composite objective functions consisting of a convex differentiable loss function plus a non-smooth regularization term, such as L-1 norm or nuclear norm, under Renyi differential privacy (RDP). To solve the problem, we propose two stochastic alternating direction method of multipliers (ADMM) algorithms: ssADMM based on gradient perturbation and mpADMM based on output perturbation. Both algorithms decompose the original problem into sub-problems that have closed-form solutions. The first algorithm, ssADMM, applies the recent privacy amplification result for RDP to reduce the amount of noise to add. The second algorithm, mpADMM, numerically computes the sensitivity of ADMM variable updates and releases the updated parameter vector at the end of each epoch. We compare the performance of our algorithms with several baseline algorithms on both real and simulated datasets. Experimental results show that, in high privacy regimes (small.), ssADMM and mpADMM outperform baseline algorithms in terms of classification and feature selection performance, respectively.

关键词： differential privacy convex optimization ADMM regularization

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Attacking and Protecting Tunneled Traffic of Smart Home Devices 20

Attacking and Protecting Tunneled Traffic of Smart Home Devi...

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10th acm conference on data and application security and privacy (CODASPY)

作者： Alshehri, Ahmed Granley, Jacob Yue, Chuan Colroado Sch Mines Golden CO 80401 USA

ISBN: (纸本)9781450371070

The number of smart home IoT (Internet of Things) devices has been growing fast in recent years. Along with the great benefits brought by smart home devices, new threats have appeared. One major threat to smart home users is the compromise of their privacy by traffic analysis (TA) attacks. Researchers have shown that TA attacks can be performed successfully on either plain or encrypted traffic to identify smart home devices and infer user activities. Tunneling traffic is a very strong countermeasure to existing TA attacks. However, in this work, we design a Signature based Tunneled Traffic Analysis (STTA) attack that can be effective even on tunneled traffic. Using a popular smart home traffic dataset, we demonstrate that our attack can achieve an 83% accuracy on identifying 14 smart home devices. We further design a simple defense mechanism based on adding uniform random noise to effectively protect against our TA attack without introducing too much overhead. We prove that our defense mechanism achieves approximate differential privacy.

关键词： Internet of Things (IoT) Traffic Analysis (TA) privacy Differential privacy Smart Homes Attacks Defenses

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Poster: A privacy-Preserving Smart Contract Vulnerability Detection Framework for Permissioned Blockchain 23

Poster: A Privacy-Preserving Smart Contract Vulnerability De...

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proceedings of the 2023 acm SIGSAC conference on Computer and Communications security

作者： Wensheng Tian Lei Zhang Shuangxi Chen Hu Wang Xiao Luo Nanhu Lab Jiaxing China Jiaxing Vocational and Technical College Jiaxing China Zhejiang Big Data Development Administration Hangzhou China Zhejiang University Hangzhou 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, etc. 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： blockchain privacy-preserving smart contract vulnerability detection

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Understanding privacy Awareness in Android App Descriptions Using Deep Learning 20

Understanding Privacy Awareness in Android App Descriptions ...

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10th acm conference on data and application security and privacy (CODASPY)

作者： Feichtner, Johannes Gruber, Stefan Graz Univ Technol Graz Austria Secure Informat Technol Ctr Austria A SIT Graz Austria

ISBN: (纸本)9781450371070

Permissions are a key factor in Android to protect users' privacy. As it is often not obvious why applications require certain permissions, developer-provided descriptions in Google Play and third-party markets should explain to users how sensitive data is processed. Reliably recognizing whether app descriptions cover permission usage is challenging due to the lack of enforced quality standards and a variety of ways developers can express privacy-related facts. We introduce a machine learning-based approach to identify critical discrepancies between developer-described app behavior and permission usage. By combining state-of-the-art techniques in natural language processing (NLP) and deep learning, we design a convolutional neural network (CNN) for text classification that captures the relevance of words and phrases in app descriptions in relation to the usage of dangerous permissions. Our system predicts the likelihood that an app requires certain permissions and can warn about descriptions in which the requested access to sensitive user data and system features is textually not represented. We evaluate our solution on 77,000 real-world app descriptions and find that we can identify individual groups of dangerous permissions with a precision between 71% and 93%. To highlight the impact of individual words and phrases, we employ a model explanation algorithm and demonstrate that our technique can successfully bridge the semantic gap between described app functionality and its access to security- and privacy-sensitive resources.

关键词： Android Machine Learning Description Permission NLP CNN

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FridgeLock: Preventing data Theft on Suspended Linux with Usable Memory Encryption 20

FridgeLock: Preventing Data Theft on Suspended Linux with Us...

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10th acm conference on data and application security and privacy (CODASPY)

作者： Franzen, Fabian Andreas, Manuel Huber, Manuel Tech Univ Munich Munich Germany Fraunhofer AISEC Garching Germany

ISBN: (纸本)9781450371070

To secure mobile devices, such as laptops and smartphones, against unauthorized physical data access, employing Full Disk Encryption (FDE) is a popular defense. This technique is effective if the device is always shut down when unattended. However, devices are often suspended instead of switched off. This leaves confidential data such as the FDE key, passphrases and user data in RAM which may be read out using cold boot, JTAG or DMA attacks. These attacks can be mitigated by encrypting the main memory during suspend. While this approach seems promising, it is not implemented on Windows or Linux. We present FridgeLock to add memory encryption on suspend to Linux. Our implementation as a Linux Kernel Module (LKM) does not require an admin to recompile the kernel. Using Dynamic Kernel Module Support (DKMS) allows for easy and fast deployment on existing Linux systems, where the distribution provides a prepackaged kernel and kernel updates. We tested our module on a range of 4.19 to 5.3 kernels and experienced a low performance impact, sustaining the system's usability. We hope that our tool leads to a more detailed evaluation of memory encryption in real world usage scenarios.

关键词： suspend time memory encryption operating systems security ram encryption data confidentiality

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