New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(logN) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property - it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(logN) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol, the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 1/2 using O(\sqrt{N}) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user's queries.
Recently, Decentralized Finance (DeFi) platforms on Ethereum are booming, and numerous traders are trying to capitalize on the opportunity for maximizing their benefits by launching front-running attacks and extracting Miner Extractable Values (MEVs) based on information in the public mempool. To protect end users from being harmed and hide transactions from the mempool, private transactions, a special type of transactions that are sent directly to miners, were invented. Private transactions have a high probability of being packed to the front positions of a block and being added to the blockc…
Cryptologic Techniques and Associated Risks in Public and Private Security. An Italian and European Union Perspective with an Overview of the Current Legal Framework
We propose a Fourier domain asymmetric cryptosystem for multimodal biometric security. One modality of biometrics (such as face) is used as the plaintext, which is encrypted by another modality of biometrics (such as fingerprint). A private key is synthesized from the encrypted biometric signature by complex spatial Fourier processing. The encrypted biometric signature is further encrypted by other biometric modalities, and the corresponding private keys are synthesized. The resulting biometric signature is privacy protected since the encryption keys are provided by the human, and hence those …
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts（马萨诸塞大学）
Many differentially private algorithms for answering database queries involve a step that reconstructs a discrete data distribution from noisy measurements. This provides consistent query answers and reduces error, but often requires space that grows exponentially with dimension. Private-PGM is a recent approach that uses graphical models to represent the data distribution, with complexity proportional to that of exact marginal inference in a graphical model with structure determined by the co-occurrence of variables in the noisy measurements. Private-PGM is highly scalable for sparse measurem…
We propose a new differentially-private decision forest algorithm that minimizes both the number of queries required, and the sensitivity of those queries. To do so, we build an ensemble of random decision trees that avoids querying the private data except to find the majority class label in the leaf nodes. Rather than using a count query to return the class counts like the current state-of-the-art, we use the Exponential Mechanism to only output the class label itself. This drastically reduces the sensitivity of the query -- often by several orders of magnitude -- which in turn reduces the am…
Privacy concerns in outsourced cloud databases have become more and more important recently and many efficient and scalable query processing methods over encrypted data have been proposed. However, there is very limited work on how to securely process top-k ranking queries over encrypted databases in the cloud. In this paper, we focus exactly on this problem: secure and efficient processing of top-k queries over outsourced databases. In particular, we propose the first efficient and provable secure top-k query processing construction that achieves adaptively CQA security. We develop an encrypt…
Differential privacy (DP) has been widely used to protect the privacy of confidential cyber physical energy systems (CPES) data. However, applying DP without analyzing the utility, privacy, and security requirements can affect the data utility as well as help the attacker to conduct integrity attacks (e.g., False Data Injection(FDI)) leveraging the differentially private data. Existing anomaly-detection-based defense strategies against data integrity attacks in DP-based smart grids fail to minimize the attack impact while maximizing data privacy and utility. To address this challenge, it is no…
The Fuqua School of Business, Duke University（杜克大学富卡商学院）；Stanford Graduate School of Business, Stanford University（斯坦福大学商学院）
We study the query complexity of a learner-private sequential learning problem, motivated by the privacy and security concerns due to eavesdropping that arise in practical applications such as pricing and Federated Learning. A learner tries to estimate an unknown scalar value, by sequentially querying an external database and receiving binary responses; meanwhile, a third-party adversary observes the learner's queries but not the responses. The learner's goal is to design a querying strategy with the minimum number of queries (optimal query complexity) so that she can accurately estimate the t…
Carnegie Mellon University（卡内基梅隆大学）；University of Minnesota（明尼苏达大学）；Google Research（谷歌研究院）；Northeastern University（东北大学）
In many statistical problems, incorporating priors can significantly improve performance. However, the use of prior knowledge in differentially private query release has remained underexplored, despite such priors commonly being available in the form of public datasets, such as previous US Census releases. With the goal of releasing statistics about a private dataset, we present PMW^Pub, which -- unlike existing baselines -- leverages public data drawn from a related distribution as prior information. We provide a theoretical analysis and an empirical evaluation on the American Community Surve…
We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma. The key idea is to randomly project the query answers to a lower dimensional space so that the distance between any two vectors of feasible query answers is preserved up to an additive error. Then we answer the projected queries using a simple noise-adding mechanism, and lift the answers up to the original dimension. Using this method, we give, for the first time, purely differentially private mechanisms with optimal worst case sample complexity under aver…
University of Sindh（信德大学）；Korea Polytechnic University（韩国理工大学）；Munster Technological University（芒斯特理工大学）；University of Johannesburg（约翰内斯堡大学）；Sungkyunkwan University（成均馆大学）；University of Bologna（博洛尼亚大学）
With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a Federated Learning and Encryption-based Private (FLE…
Single Secret Leader Elections have recently been proposed as an improved leader election mechanism for proof-of-stake (PoS) blockchains. However, the security gain they provide has not been quantified. In this work, we present a comparison of PoS longest-chain protocols that are based on Single Secret Leader Elections (SSLE) - that elect exactly one leader per round - versus those based on Probabilistic Leader Elections (PLE) - where one leader is elected on expectation. Our analysis shows that when considering the private attack - the worst attack on longest-chain protocols - the security ga…
Information sharing among organizations has been gaining attention as a method for improving cybersecurity. However, the associated disclosure costs act as deterrents for firms' voluntary cooperation. In this work, we take a game-theoretic approach to understanding firms' incentives in these agreements. We propose the design of inter-temporal incentives (i.e. conditioning future cooperation on past interactions). Specifically, we show that incentives for full cooperation can be designed if firms share their private assessments of other firms' disclosure decisions through a common communication…
FastQuery: Communication-efficient Embedding Table Query for Private LLM Inference
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Beijing Advanced Innovation Center for Integrated Circuits（北京集成电路高精尖创新中心）；Institute of Electronic Design Automation, Peking University（北京大学电子设计自动化研究所）
Analyses that fulfill differential privacy provide plausible deniability to individuals while allowing analysts to extract insights from data. However, beyond an often acceptable accuracy tradeoff, these statistical disclosure techniques generally inhibit the verifiability of the provided information, as one cannot check the correctness of the participants' truthful information, the differentially private mechanism, or the unbiased random number generation. While related work has already discussed this opportunity, an efficient implementation with a precise bound on errors and corresponding pr…
In this work, we investigate if statistical privacy can enhance the performance of ORAM mechanisms while providing rigorous privacy guarantees. We propose a formal and rigorous framework for developing ORAM protocols with statistical security viz., a differentially private ORAM (DP-ORAM). We present Root ORAM, a family of DP-ORAMs that provide a tunable, multi-dimensional trade-off between the desired bandwidth overhead, local storage and system security. We theoretically analyze Root ORAM to quantify both its security and performance. We experimentally demonstrate the benefits of Root ORAM an…
In this work, we extend the Constructive Cryptography (CC) framework introduced by Maurer in 2011 so as to handle interactive protocols. We design and construct a so-called {\em Interactive Server Memory Resource} (ISMR), that is an augmented version of the basic instantiation of a client-server protocol in CC, namely the Server Memory Resource. We then apply our ISMR construction to two types of interactive cryptographic protocols for remote storage : Updatable Encryption (UE) and Private Information Retrieval (PIR). Concerning UE, our results are a composable version of those protocols, clar…
Amazon AWS AI/ML（亚马逊云科技人工智能与机器学习部门）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy. Our algorithm makes adaptive use of a continuous relaxation of the Projection Mechanism, which answers queries on the private dataset using simple perturbation, and then attempts to find the synthetic dataset that most closely matches the noisy answers. We use a continuous relaxation of the synthetic dataset domain which makes the projection loss differentiable, and allows us to use efficient ML optimization techniques a…
We propose a novel algorithm to ensure $ε$-differential privacy for answering range queries on trajectory data. In order to guarantee privacy, differential privacy mechanisms add noise to either data or query, thus introducing errors to queries made and potentially decreasing the utility of information. In contrast to the state-of-the-art, our method achieves significantly lower error as it is the first data- and query-aware approach for such queries. The key challenge for answering range queries on trajectory data privately is to ensure an accurate count. Simply representing a trajectory as a…
In 2012, De Cristofaro et al. proposed a protocol to calculate the Private Set Intersection and Union cardinality(PSI-CA and PSU-CA). This protocol's security is based on the famous DDH assumption. Since its publication, it has gained lots of popularity because of its efficiency(linear complexity in computation and communication) and concision. So far, it's still considered one of the most efficient PSI-CA protocols and the most cited(more than 170 citations) PSI-CA paper based on the Google Scholar search. However, when we tried to implement this protocol, we couldn't get the correct result o…
Security and privacy of the Internet Domain Name System (DNS) have been longstanding concerns. Recently, there is a trend to protect DNS traffic using Transport Layer Security (TLS). However, at least two major issues remain: (1) how do clients authenticate DNS-over-TLS endpoints in a scalable and extensible manner; and (2) how can clients trust endpoints to behave as expected? In this paper, we propose a novel Private DNS-over-TLS (PDoT ) architecture. PDoT includes a DNS Recursive Resolver (RecRes) that operates within a Trusted Execution Environment (TEE). Using Remote Attestation, DNS clie…
The societal and economical consequences surrounding Big Data-driven platforms have increased the call for decentralized solutions. However, retrieving and querying data in more decentralized environments requires fundamentally different approaches, whose properties are not yet well understood. Link Traversal-based Query Processing (LTQP) is a technique for querying over decentralized data networks, in which a client-side query engine discovers data by traversing links between documents. Since decentralized environments are potentially unsafe due to their non-centrally controlled nature, there…
Differential privacy (DP) allows data analysts to query databases that contain users' sensitive information while providing a quantifiable privacy guarantee to users. Recent interactive DP systems such as APEx provide accuracy guarantees over the query responses, but fail to support a large number of queries with a limited total privacy budget, as they process incoming queries independently from past queries. We present an interactive, accuracy-aware DP query engine, CacheDP, which utilizes a differentially private cache of past responses, to answer the current workload at a lower privacy budg…
A lot of research has been focused on secure outsourcing of biometric identification in the context of cloud computing. In such schemes, both the encrypted biometric database and the identification process are outsourced to the cloud. The ultimate goal is to protect the security and privacy of the biometric database and the query templates. Security analysis shows that previous schemes suffer from the enrolment attack and unnecessarily expose more information than needed. In this paper, we propose a new secure outsourcing scheme aims at enhancing the security from these two aspects. First, bes…
We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially new about the data of others. First, we introduce perfect non-stochastic privacy in a two-party scenario. Perfect privacy amounts to conditional unrelatedness of the query response and the private uncertain variable of other individuals conditioned on the uncertain variable of a given entity. We show that perfect privacy can be achieved for queries that are functions of the common uncertain variable, a generalizatio…
By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and a…
Internet of Things (IoT)-based smart home applications are rising in popularity. However, this trend attracts malicious activity, which causes cost-efficient security to be in high demand. This paper proposes a low-end design that reinforces the security of a home network. It uses private blockchain technology and localization via RSSI-based trilateration. We investigated the benefits of private blockchains over their public counterpart, and we improve the precision of the localization algorithm by testing it against different wireless technologies. The results conclude that using a private bl…
Privately learning statistics of events on devices can enable improved user experience. Differentially private algorithms for such problems can benefit significantly from interactivity. We argue that an aggregation protocol can enable an interactive private federated statistics system where user's devices maintain control of the privacy assurance. We describe the architecture of such a system, and analyze its security properties.
Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists o…
