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Researcher profile

Florian Kerschbaum

Florian Kerschbaum contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityMachine LearningArtificial IntelligenceComputation and LanguageDatabasesDistributed, Parallel, and Cluster Computing

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Published work

14 published item(s)

preprint2026arXiv

Private Vertical Federated Inference for Time-Series

Institutions may benefit from collaborative inference on time-series data. In settings where privacy is necessary, multi-party computation (MPC) is a straightforward approach to providing strong guarantees, yet it remains prohibitively expensive and scales poorly with modern transformer architectures. Vertical Federated Learning (VFL) offers efficiency but suffers from privacy leakage at the embedding level, and securing the entire VFL model head via MPC remains prohibitively slow and communication-heavy for larger models. To enable practical, secure inference at scale, we propose "Public/Private Hybrid Head-VFL" (PPHH-VFL). This hybrid architecture splits the model head into an efficient plaintext public head and a secure, lightweight MPC private head. By applying adversarial training to the public embeddings, we mitigate privacy leakage; concurrently, the small private head securely preserves the flow of sensitive information needed for high downstream utility. Empirical evaluations on models ranging up to 86 million parameters demonstrate that PPHH-VFL accelerates inference by up to six orders of magnitude compared to end-to-end MPC. Compared to a standard VFL+MPC baseline, our approach scales significantly better, achieving a speedup of up to 44.4x in WAN and a 91.2x reduction in communication costs (dropping from 1.7 GB to 19 MB per batch), while simultaneously improving downstream classification accuracy by 2.50% and regression RMSE by 40.7%.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Identifying and Mitigating the Security Risks of Generative AI

Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive, but is rather an attempt to synthesize some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides both a launching point for a discussion on this important topic as well as interesting problems that the research community can work to address.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Assessing Differentially Private Variational Autoencoders under Membership Inference

We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders. Our work complements previous work in two aspects. First, we evaluate the the strong reconstruction MI attack against Variational Autoencoders under differential privacy. Second, we address the data scientist's challenge of setting privacy parameter epsilon, which steers the differential privacy strength and thus also the privacy-accuracy trade-off. In our experimental study we consider image and time series data, and three local and central differential privacy mechanisms. We find that the privacy-accuracy trade-offs strongly depend on the dataset and model architecture. We do rarely observe favorable privacy-accuracy trade-off for Variational Autoencoders, and identify a case where LDP outperforms CDP.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Constant-weight PIR: Single-round Keyword PIR via Constant-weight Equality Operators

Equality operators are an essential building block in tasks over secure computation such as private information retrieval. In private information retrieval (PIR), a user queries a database such that the server does not learn which element is queried. In this work, we propose \emph{equality operators for constant-weight codewords}. A constant-weight code is a collection of codewords that share the same Hamming weight. Constant-weight equality operators have a multiplicative depth that depends only on the Hamming weight of the code, not the bit-length of the elements. In our experiments, we show how these equality operators are up to 10 times faster than existing equality operators. Furthermore, we propose PIR using the constant-weight equality operator or \emph{constant-weight PIR}, which is a PIR protocol using an approach previously deemed impractical. We show that for private retrieval of large, streaming data, constant-weight PIR has a smaller communication complexity and lower runtime compared to SEALPIR and MulPIR, respectively, which are two state-of-the-art solutions for PIR. Moreover, we show how constant-weight PIR can be extended to keyword PIR. In keyword PIR, the desired element is retrieved by a unique identifier pertaining to the sought item, e.g., the name of a file. Previous solutions to keyword PIR require one or multiple rounds of communication to reduce the problem to normal PIR. We show that constant-weight PIR is the first practical single-round solution to single-server keyword PIR.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

IHOP: Improved Statistical Query Recovery against Searchable Symmetric Encryption through Quadratic Optimization

Effective query recovery attacks against Searchable Symmetric Encryption (SSE) schemes typically rely on auxiliary ground-truth information about the queries or dataset. Query recovery is also possible under the weaker statistical auxiliary information assumption, although statistical-based attacks achieve lower accuracy and are not considered a serious threat. In this work we present IHOP, a statistical-based query recovery attack that formulates query recovery as a quadratic optimization problem and reaches a solution by iterating over linear assignment problems. We perform an extensive evaluation with five real datasets, and show that IHOP outperforms all other statistical-based query recovery attacks under different parameter and leakage configurations, including the case where the client uses some access-pattern obfuscation defenses. In some cases, our attack achieves almost perfect query recovery accuracy. Finally, we use IHOP in a frequency-only leakage setting where the client's queries are correlated, and show that our attack can exploit query dependencies even when PANCAKE, a recent frequency-hiding defense by Grubbs et al., is applied. Our findings indicate that statistical query recovery attacks pose a severe threat to privacy-preserving SSE schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics

Key-value data is a naturally occurring data type that has not been thoroughly investigated in the local trust model. Existing local differentially private (LDP) solutions for computing statistics over key-value data suffer from the inherent accuracy limitations of each user adding their own noise. Multi-party computation (MPC) maintains better accuracy than LDP and similarly does not require a trusted central party. However, naively applying MPC to key-value data results in prohibitively expensive computation costs. In this work, we present selective multi-party computation, a novel approach to distributed computation that leverages DP leakage to efficiently and accurately compute statistics over key-value data. By providing each party with a view of a random subset of the data, we can capture subtractive noise. We prove that our protocol satisfies pure DP and is provably secure in the combined DP/MPC model. Our empirical evaluation demonstrates that we can compute statistics over 10,000 keys in 20 seconds and can scale up to 30 servers while obtaining results for a single key in under a second.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Limits of Word Level Differential Privacy

As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very important contributions, have various advantages over other techniques and do show anonymization capabilities, they have several shortcomings. In this paper, we investigate these weaknesses and demonstrate significant mathematical constraints diminishing the theoretical privacy guarantee as well as major practical shortcomings with regard to the protection against deanonymization attacks, the preservation of content of the original sentences as well as the quality of the language output. Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also offers a formal privacy guarantee. We evaluate the performance of our method via thorough experimentation and demonstrate superior performance over the discussed mechanisms.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Deep Neural Network Fingerprinting by Conferrable Adversarial Examples

In Machine Learning as a Service, a provider trains a deep neural network and gives many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust method to determine whether a suspect model is a surrogate of their model. We propose a fingerprinting method for deep neural network classifiers that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs. These inputs are a subclass of transferable adversarial examples which we call conferrable adversarial examples that exclusively transfer with a target label from a source model to its surrogates. We propose a new method to generate these conferrable adversarial examples. We present an extensive study on the irremovability of our fingerprint against fine-tuning, weight pruning, retraining, retraining with different architectures, three model extraction attacks from related work, transfer learning, adversarial training, and two new adaptive attacks. Our fingerprint is robust against distillation, related model extraction attacks, and even transfer learning when the attacker has no access to the model provider's dataset. Our fingerprint is the first method that reaches a ROC AUC of 1.0 in verifying surrogates, compared to a ROC AUC of 0.63 by previous fingerprints.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Obfuscated Access and Search Patterns in Searchable Encryption

Searchable Symmetric Encryption (SSE) allows a data owner to securely outsource its encrypted data to a cloud server while maintaining the ability to search over it and retrieve matched documents. Most existing SSE schemes leak which documents are accessed per query, i.e., the so-called access pattern, and thus are vulnerable to attacks that can recover the database or the queried keywords. Current techniques that fully hide access patterns, such as ORAM or PIR, suffer from heavy communication or computational costs, and are not designed with search capabilities in mind. Recently, Chen et al. (INFOCOM'18) proposed an obfuscation framework for SSE that protects the access pattern in a differentially private way with a reasonable utility cost. However, this scheme leaks the so-called search pattern, i.e., how many times a certain query is performed. This leakage makes the proposal vulnerable to certain database and query recovery attacks. In this paper, we propose OSSE (Obfuscated SSE), an SSE scheme that obfuscates the access pattern independently for each query performed. This in turn hides the search pattern and makes our scheme resistant against attacks that rely on this leakage. Under certain reasonable assumptions, our scheme has smaller communication overhead than ORAM-based SSE. Furthermore, our scheme works in a single communication round and requires very small constant client-side storage. Our empirical evaluation shows that OSSE is highly effective at protecting against different query recovery attacks while keeping a reasonable utility level. Our protocol provides significantly more protection than the proposal by Chen et al.~against some state-of-the-art attacks, which demonstrates the importance of hiding search patterns in designing effective privacy-preserving SSE schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

PCOR: Private Contextual Outlier Release via Differentially Private Search

Outlier detection plays a significant role in various real world applications such as intrusion, malfunction, and fraud detection. Traditionally, outlier detection techniques are applied to find outliers in the context of the whole dataset. However, this practice neglects contextual outliers, that are not outliers in the whole dataset but in some specific neighborhoods. Contextual outliers are particularly important in data exploration and targeted anomaly explanation and diagnosis. In these scenarios, the data owner computes the following information: i) The attributes that contribute to the abnormality of an outlier (metric), ii) Contextual description of the outlier's neighborhoods (context), and iii) The utility score of the context, e.g. its strength in showing the outlier's significance, or in relation to a particular explanation for the outlier. However, revealing the outlier's context leaks information about the other individuals in the population as well, violating their privacy. We address the issue of population privacy violations in this paper, and propose a solution for the two main challenges. In this setting, the data owner is required to release a valid context for the queried record, i.e. a context in which the record is an outlier. Hence, the first major challenge is that the privacy technique must preserve the validity of the context for each record. We propose techniques to protect the privacy of individuals through a relaxed notion of differential privacy to satisfy this requirement. The second major challenge is applying the proposed techniques efficiently, as they impose intensive computation to the base algorithm. To overcome this challenge, we propose a graph structure to map the contexts to, and introduce differentially private graph search algorithms as efficient solutions for the computation problem caused by differential privacy techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks

Watermarking of deep neural networks (DNN) can enable their tracing once released by a data owner. In this paper, we generalize white-box watermarking algorithms for DNNs, where the data owner needs white-box access to the model to extract the watermark. White-box watermarking algorithms have the advantage that they do not impact the accuracy of the watermarked model. We propose Robust whIte-box GAn watermarking (RIGA), a novel white-box watermarking algorithm that uses adversarial training. Our extensive experiments demonstrate that the proposed watermarking algorithm not only does not impact accuracy, but also significantly improves the covertness and robustness over the current state-of-art.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Assessing differentially private deep learning with Membership Inference

Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible protection is offered by anonymization of the training data or training function with differential privacy. However, data scientists can choose between local and central differential privacy and need to select meaningful privacy parameters $ε$ which is challenging for non-privacy experts. We empirically compare local and central differential privacy mechanisms under white- and black-box membership inference to evaluate their relative privacy-accuracy trade-offs. We experiment with several datasets and show that this trade-off is similar for both types of mechanisms. This suggests that local differential privacy is a sound alternative to central differential privacy for differentially private deep learning, since small $ε$ in central differential privacy and large $ε$ in local differential privacy result in similar membership inference attack risk.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

EncDBDB: Searchable Encrypted, Fast, Compressed, In-Memory Database using Enclaves

Data confidentiality is an important requirement for clients when outsourcing databases to the cloud. Trusted execution environments, such as Intel SGX, offer an efficient, hardware-based solution to this cryptographic problem. Existing solutions are not optimized for column-oriented, in-memory databases and pose impractical memory requirements on the enclave. We present EncDBDB, a novel approach for client-controlled encryption of a column-oriented, in-memory databases allowing range searches using an enclave. EncDBDB offers nine encrypted dictionaries, which provide different security, performance and storage efficiency tradeoffs for the data. It is especially suited for complex, read-oriented, analytic queries, e.g., as present in data warehouses. The computational overhead compared to plaintext processing is within a millisecond even for databases with millions of entries and the leakage is limited. Compressed encrypted data requires less space than a corresponding plaintext column. Furthermore, the resulting code - and data - in the enclave is very small reducing the potential for security-relevant implementation errors and side-channel leakages.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Plug-in privacy for Smart Metering billing

Traditional electricity meters are replaced by Smart Meters in customers' households. Smart Meters collects fine-grained utility consumption profiles from customers, which in turn enables the introduction of dynamic, time-of-use tariffs. However, the fine-grained usage data that is compiled in this process also allows to infer the inhabitant's personal schedules and habits. We propose a privacy-preserving protocol that enables billing with time-of-use tariffs without disclosing the actual consumption profile to the supplier. Our approach relies on a zero-knowledge proof based on Pedersen Commitments performed by a plug-in privacy component that is put into the communication link between Smart Meter and supplier's back-end system. We require no changes to the Smart Meter hardware and only small changes to the software of Smart Meter and back-end system. In this paper we describe the functional and privacy requirements, the specification and security proof of our solution and give a performance evaluation of a prototypical implementation.

0 citations0 reviewsNo code linkedOpen access