Privacy Properties Research Articles

Security and Privacy on Generative Data in AIGC: A Survey

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we show some representative benchmarks, present a statistical analysis, and summarize the potential exploration directions from each of theses properties.

Deniability in Automated Contact Tracing: Impossibilities and Possibilities

Automated contact tracing (ACT) emerged as a promising measure to curb the spread of Covid-19. Users enable ACT on their smartphones to automatically record contacts with other users. If a user tests positive for the disease, they report their diagnosis to alert their contacts. Designing effective ACT protocols is challenging since they need to be efficient and secure while also ensuring users' privacy. As ACT protocols necessarily leak some information by design, defining privacy is difficult. For example, a user cannot deny having met another user. Ideally, however, the user can plausibly deny everything else, in particular, when they met. We call this privacy property contact-time deniability. While some early works discussed contact-time deniability informally, it has received little attention since then. We investigate deniability from a rigorous, theoretical point of view and arrive at the following impossibility result: A decentralized protocol with unidirectional communication cannot be contact-time deniable and replay-secure. This holds even if malicious users treat smartphones as black-boxes. Unidirectional protocols are usually very efficient and many proposals are unidirectional, e.g., the widely-deployed Google-Apple Exposure Notifications. So the impossibility result considerably constrains the design space of efficient, secure, and private ACT protocols. However, it can also be used as a guide; we discuss several possibilities to achieve contact-time deniability in practice.

Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.

On the Privacy of Elementary Matrices Masking-Based Verifiable (Outsourced) Computation

Privacy-preserving Verifiable (outsourced) Computation (PVC) for face recognition is a significant research topic in the information security community. Recently, an efficient elementary matrices masking-based PVC protocol for face recognition has been published in IEEE Transactions on Dependable and Secure Computing [2]. In this paper, we analyze the privacy property of this protocol, and demonstrate that the output distribution of the problem generation algorithm in this protocol is not computationally indistinguishable from the uniform distribution over a matrix set, which breaks the original consequence (see Theorem 1). We introduce a formal definition of a privacy model for a PVC protocol and prove that the targeted PVC protocol does not hold privacy under this model. We then present our experimental results to support our theoretical analyses.

FedComm: Federated Learning as a Medium for Covert Communication

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without revealing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">actual</i> private training data. To date, a substantial amount of research has investigated the security and privacy properties of FL, resulting in a plethora of innovative attack and defense strategies. This paper thoroughly investigates the communication capabilities of an FL scheme. In particular, we show that a party involved in the FL learning process can use FL as a covert communication medium to send an arbitrary message. We introduce FedComm, a novel covert-communication technique that enables robust sharing and transfer of targeted payloads within the FL framework. Our extensive theoretical and empirical evaluations show that FedComm provides a stealthy communication channel, with minimal disruptions to the training process. Our experiments show that FedComm successfully delivers 100% of a payload in the order of kilobits before the FL procedure converges. Our evaluation also shows that FedComm is independent of the application domain and the neural network architecture used by the underlying FL scheme.

VPFL: Enabling verifiability and privacy in federated learning with zero-knowledge proofs

Federated learning has become increasingly popular in recent years and is widely applied to various fields of machine learning. However, the localized execution of federated learning lacks visibility of the training process to a third party (e.g., the model user or auditor), which raises the need to verify the training process. Privacy concerns over the public release of sensitive data for verification purposes are a prominent issue. Therefore, enabling a public verification of the training process without revealing sensitive data is a challenge. In this paper, we focus on verifiability and privacy in federated learning and propose a verifiable and privacy-preserving federated learning scheme (VPFL). We first employ zero-knowledge proofs to allow a third party to publicly verify the training process, which enhances the transparency of the training process and the reliability of the model. Then, to further protect the privacy of sensitive data, we exploit a commitment scheme to ensure that no information about sensitive data is leaked to a third party. We conduct extensive experiments to evaluate the performance of our scheme. For federated learning with 100 clients, our scheme only takes 13.1s to generate evidence and 8.8s to verify it. In addition, we compared our scheme with other schemes, and we observed that our scheme satisfies both the security properties of verifiability and privacy.

DP-GSGLD: A Bayesian optimizer inspired by differential privacy defending against privacy leakage in federated learning

Stochastic Gradient Langevin Dynamics (SGLD) is believed to preserve differential privacy as its intrinsic attribute since it obtain randomness from posterior sampling and natural noise. In this paper, we propose Differentially Private General Stochastic Gradient Langevin Dynamics (DP-GSGLD), a novel variant of SGLD which realizes gradient estimation in parameter updating through Bayesian sampling. We introduce the technique of parameter clipping and prove that DP-GSGLD satisfies the property of Differential Privacy (DP). We conduct experiments on several image datasets for defending against gradient attack that is commonly appeared in the scenario of federated learning. The results demonstrate that DP-GSGLD can decrease the time for model training and achieve higher accuracy under the same privacy level.

Enabling Fast and Privacy-Preserving Broadcast Authentication With Efficient Revocation for Inter-Vehicle Connections

Many vehicular applications, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by vehicles. In the absence of a secure authentication scheme, invalid spatial-temporal messages may be sent out by malicious vehicles. Meanwhile, malicious applications may also collect a lot of personal information from spatial-temporal messages. Since inter-vehicle connections are often deployed in high-moving traffic, any authentication must be implemented in real-time. To meet all these properties, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) scheme for inter-vehicle connections. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is mostly designed on hash chains and an entropy-based commitment, and is able to secure periodic spatial-temporal messages. FastTrust also protects vehicles' privacy by deploying a pseudonym-varying scheduling mechanism to satisfy the anonymity and unlinkability requirements. Finally, in order to efficiently isolate malicious vehicles, a lightweight certificate management scheme is proposed for the limited bandwidth of vehicular networks. We provide analytical evaluations to show that our FastTrust achieves the security and privacy properties. Extensive validations are done to show that FastTrust can authenticate dozens of times faster than the existing signature algorithms, and isolate malicious vehicles at a low cost in terms of communication and computational resources.

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contains private information, could be inferred by an eavesdropper. In this paper, a privacy-preserving distributed average observer is proposed, which is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. The proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

Dijitalleşme sürecinde tüketicilerin bankacılık hizmet tercihini şekillendiren unsurlara yönelik bir inceleme

With a focus on banking services specifically, this study attempts to investigate the variables influencing consumers' choices for services during the digitalization process. A review of the literature was done for this purpose, and the data gathered was arranged and given as a summary of the literature. Within the scope of the research, firstly, the factors shaping consumers' digital banking service preference were discussed in a general framework, subsequently the subject was detailed around the determined headings. In this context, the subject is discussed in terms of search, experience and credence properties of service products, perceived risk, privacy, and security concern (and privacy paradox) and consumer characteristics, suggestions for future research are presented in the conclusion section. With the goal of addressing the consumer's service choice in the digitalization process with a holistic approach and current concepts, particularly for financial services, it is assessed that the study will provide a distinctive contribution. The study is the product of a comprehensive literature review; therefore care was taken to ensure that the elements identified were comprehensive, both in terms of digital services in general and in banking services in particular. However, it is possible to talk about different factors that are likely to shape consumer behavior in the digitalization process and are beyond the scope of this study. In addition, the rapid change and transformation environment caused by digitalization brings with it the need to constantly address the issue with new research.

Security and Privacy Requirements for RFID Schemes in Healthcare: Case Studies, Solutions, and Challenges

RFID technology has proven very useful in many healthcare applications, starting with medical asset tracking, continuing with staff tracking, and reaching medical event and procedure tracking. Security and privacy properties play a fundamental role in this range of applications. The protocols proposed so far highlights many techniques for analyzing these properties, making it difficult to compare the protocols with each other most of the time. In the case of lightweight protocols, the comparisons are even more challenging to achieve because these resort to idealizations that usually have no practical support, such as protocols based on physically unclonable functions (PUFs), or because no security and privacy model is known so far. This paper draws attention to the HPVP model as a possibility for uniformly studying healthcare RFID protocols’ security and privacy properties. We highlight two general patterns that, once encountered in RFID protocols, show that they do not satisfy privacy or mutual authentication requirements. We then illustrate these templates through several proposed RFID protocols for use in healthcare. Then, we propose a Classification of healthcare services based on an extended hierarchy of HPVP privacy classes, and we highlight RFID protocols that reach the respective levels of privacy.

SGXonerate:Finding (and Partially Fixing) Privacy Flaws in TEE-based Smart Contract Platforms Without Breaking the TEE

TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks. The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases. Second, the importance of state consistency has been underappreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest.

DeVoS: Deniable Yet Verifiable Vote Updating

Internet voting systems are supposed to meet the same high standards as traditional paper-based systems when used in real political elections: freedom of choice, universal and equal suffrage, secrecy of the ballot, and independent verifiability of the election result. Although numerous Internet voting systems have been proposed to achieve these challenging goals simultaneously, few come close in reality. We propose a novel publicly verifiable and practically efficient Internet voting system, DeVoS, that advances the state of the art. The main feature of DeVoS is its ability to protect voters' freedom of choice in several dimensions. First, voters in DeVoS can intuitively update their votes in a way that is deniable to observers but verifiable by the voters; in this way voters can secretly overwrite potentially coerced votes. Second, in addition to (basic) vote privacy, DeVoS also guarantees strong participation privacy by end-to-end hiding which voters have submitted ballots and which have not. Finally, DeVoS is fully compatible with Perfectly Private Audit Trail, a state-of-the-art Internet voting protocol with practical everlasting privacy. In combination, DeVoS offers a new way to secure free Internet elections with strong and long-term privacy properties.

A Location-Privacy-Aware Taxi-Hailing System: Adaptive Differential Privacy-Based Dynamic Incentive Method

Nowadays location-based service (LBS) has become an important service in people’s daily life. Online taxi-hailing system (DiDi, Uber, etc.) is one of the most common LBS system. As the scale of online taxi services continues to expand, some problems have gradually emerged. Specifically, the uncertainty of taxis and passengers makes it difficult to match them effectively, and the passengers’ location privacy will be threatened from both internal and external of the system. In this paper, we first proposed a Bayesian-based location privacy inference attack method from external adversary’s point of view. After that, an adaptive differential privacy-based dynamic incentive method was proposed. Firstly, an adaptivity clocking area division method was proposed to resist the internal privacy threat. Secondly, a dynamic incentive bidding method was proposed to deal with the trading issue. Thirdly, an exponential-based matching method was proposed to resist the external inference privacy threat. Further, theoretical proofs show that the proposed method satisfy the privacy properties of k-anonymity, 2μ1ε-differential privacy, and the economic properties of incentive compatibility, individual rationality. Finally, the experimental results show that the inference attack would achieve a maximum attack success rate of 95% while ensuring the accuracy within 150 meters, and the proposed adaptive differential privacy-based dynamic incentive method can not only provide a good economic performance in terms of satisfaction ratio, social welfare and travel distance, but also can resist internal privacy threat with less than 1% of privacy leakage probability and reduce the success rate of external privacy inference attacks to 25%.

Dilations and information flow axioms in categorical probability

AbstractWe study the positivity and causality axioms for Markov categories as properties of dilations and information flow and also develop variations thereof for arbitrary semicartesian monoidal categories. These help us show that being a positive Markov category is merely an additional property of a symmetric monoidal category (rather than extra structure). We also characterize the positivity of representable Markov categories and prove that causality implies positivity, but not conversely. Finally, we note that positivity fails for quasi-Borel spaces and interpret this failure as a privacy property of probabilistic name generation.

Robust Fingerprint of Location Trajectories Under Differential Privacy.

Location-based services have brought significant convenience to people in their daily lives, and the collected location data are also in high demand. However, directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To address this, we propose a novel fingerprinting scheme that simultaneously identifies unauthorized redistribution of location datasets and provides differential privacy guarantees for the shared data. Observing data utility degradation due to differentially-private mechanisms, we introduce a utility-focused post-processing scheme to regain spatiotemporal correlations between points in a location trajectory. We further integrate this post-processing scheme into our fingerprinting scheme as a sampling method. The proposed fingerprinting scheme alleviates the degradation in the utility of the shared dataset due to the noise introduced by differentially-private mechanisms (i.e., adds the fingerprint by preserving the publicly known statistics of the data). Meanwhile, it does not violate differential privacy throughout the entire process due to immunity to post-processing, a fundamental property of differential privacy. Our proposed fingerprinting scheme is robust against known and well-studied attacks against a fingerprinting scheme including random flipping attacks, correlation-based flipping attacks, and collusions among multiple parties, which makes it hard for the attackers to infer the fingerprint codes and avoid accusation. Via experiments on two real-life location datasets and two synthetic ones, we show that our scheme achieves high fingerprinting robustness and outperforms existing approaches. Besides, the proposed fingerprinting scheme increases data utility for differentially-private datasets, which is beneficial for data analyzers.

Save The Implicit Flow? Enabling Privacy-Preserving RP Authentication in OpenID Connect

OpenID Connect (OIDC) is a Single Sign-On (SSO) protocol that allows users to authenticate to various Relying Parties (RPs) via an Identity Provider (IdP). The main drawback of SSO is its lack of privacy, as the IdP learns the RP’s identity at each user’s login. OIDC supports several protocol flows, of which only one, the Implicit Flow, gives hope for any privacy, as it does not require direct communication between the IdP and RP. This design was initially intended for RPs with technical limitations that prevent them from storing credentials and thus authenticating to the IdP. However, RP authentication is crucial to ensure that users only access properly registered RPs. As a result, the Implicit Flow is being discussed to be excluded from the OAuth specification on which OIDC is based. This paper demonstrates a privacy-preserving approach incorporating RP authentication into the Implicit Flow. The IdP can restrict its service to authenticated RPs and tie each authentication token to a specific user and RP without acquiring knowledge of which user is accessing which RP. We formally define the desired security and privacy properties of such an authenticated Implicit Flow, propose a provably secure construction from generic building blocks, and report on an implementation of our scheme

Hybrid post-quantum Transport Layer Security formal analysis in Maude-NPA and its parallel version.

This article presents a security formal analysis of the hybrid post-quantum Transport Layer Security (TLS) protocol, a quantum-resistant version of the TLS protocol proposed by Amazon Web Services as a precaution in dealing with future attacks from quantum computers. In addition to a classical key exchange algorithm, the proposed protocol uses a post-quantum key encapsulation mechanism, which is believed invulnerable under quantum computers, so the protocol's key negotiation is called the hybrid key exchange scheme. One of our assumptions about the intruder's capabilities is that the intruder is able to break the security of the classical key exchange algorithm by utilizing the power of large quantum computers. For the formal analysis, we use Maude-NPA and a parallel version of Maude-NPA (called Par-Maude-NPA) to conduct experiments. The security properties under analysis are (1) the secrecy property of the shared secret key established between two honest principals with the classical key exchange algorithm, (2) a similar secrecy property but with the post-quantum key encapsulation mechanism, and (3) the authentication property. Given the time limit T = 1,722h (72 days), Par-Maude-NPA found a counterexample of (1) at depth 12 in T, while Maude-NPA did not find it in T. At the same time T, Par-Maude-NPA did not find any counterexamples of (2) and (3) up to depths 12 and 18, respectively, and neither did Maude-NPA. Therefore, the protocol does not enjoy (1), while it enjoys (2) and (3) up to depths 12 and 18, respectively. Subsequently, the secrecy property of the master secret holds for the protocol up to depth 12.

Enhancing Data Protection in Dynamic Consent Management Systems: Formalizing Privacy and Security Definitions with Differential Privacy, Decentralization, and Zero-Knowledge Proofs.

Dynamic consent management allows a data subject to dynamically govern her consent to access her data. Clearly, security and privacy guarantees are vital for the adoption of dynamic consent management systems. In particular, specific data protection guarantees can be required to comply with rules and laws (e.g., the General Data Protection Regulation (GDPR)). Since the primary instantiation of the dynamic consent management systems in the existing literature is towards developing sustainable e-healthcare services, in this paper, we study data protection issues in dynamic consent management systems, identifying crucial security and privacy properties and discussing severe limitations of systems described in the state of the art. We have presented the precise definitions of security and privacy properties that are essential to confirm the robustness of the dynamic consent management systems against diverse adversaries. Finally, under those precise formal definitions of security and privacy, we have proposed the implications of state-of-the-art tools and technologies such as differential privacy, blockchain technologies, zero-knowledge proofs, and cryptographic procedures that can be used to build dynamic consent management systems that are secure and private by design.

A Survey on Privacy-Preserving Electronic Toll Collection Schemes for Intelligent Transportation Systems

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance the government’s road infrastructure but also it can play a crucial role in pollution reduction and congestion management. As most of the traditional ETC schemes (ETCS) require identifying their users, they enable location tracking. This violates user privacy and poses challenges regarding the compliance of such systems with privacy regulations such as the EU General Data Protection Regulation (GDPR). So far, several privacy-preserving ETC schemes have been proposed. To the best of our knowledge, this is the first survey that systematically reviews and compares various characteristics of these schemes, including components, technologies, security properties, privacy properties, and attacks on ETCS. This survey first categorizes the ETCS based on two technologies, GNSS and DSRC. Then under these categories, the schemes are classified based on whether they provide formal proof of security and support security analysis. We also demonstrate which schemes specifically are/are not resistant to collusion and physical attacks. Then, based on these classifications, several limitations and shortcomings in privacy-preserving ETCS are revealed. Finally, we identify several directions for future research.