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Nowadays, data is mostly collected not by state actors but by businesses. In 2010, the German Constitutional Court held that the legislator has to evaluate the overall level of surveillance in Germany before enacting new data retention obligations. In light of the recent rejuvenised discussions about data retention and a general surveillance account, this text explores whether such an account needs to consider private data pools and what is required for a successful evaluation.

We continue a line of research initiated in Dinur and Nissim (2003); Dwork and Nissim (2004); and Blum et al. (2005) on privacy-preserving statistical databases.
Consider a trusted server that holds a database of sensitive information. Given a query function $f$ mapping databases to reals, the so-called {\em true answer} is the result of applying $f$ to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user.
Previous work focused on the case of noisy sums, in which $f = \sum_i g(x_i)$, where $x_i$ denotes the $i$th row of the database and $g$ maps database rows to $[0,1]$. We extend the study to general functions $f$, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the {\em sensitivity} of the function $f$. Roughly speaking, this is the amount that any single argument to $f$ can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case.
The first step is a very clean definition of privacy---now known as differential privacy---and measure of its loss. We also provide a set of tools for designing and combining differentially private algorithms, permitting the construction of complex differentially private analytical tools from simple differentially private primitives.
Finally, we obtain separation results showing the increased value of interactive statistical release mechanisms over non-interactive ones.

Data about reproduction is tracked and gathered by many companies worldwide without people's awareness, which has profound consequences for people's reproductive rights.

Japan's data protection framework faces significant challenges emerging from corporate structures as well as inadequate defences against human actors within the chain of data custody. Indian regulators can study and learn from the Japanese experience on the creation of a legal framework that takes due consideration of the norms of free enterprise while ensuring the safety and sanctity of personal data.

International audience ; Internet of Things (IoT) is one of the key technologies in the industry 4.0 era and promotes the interconnection of numerous data sources in several sectors such as ecology, agriculture, or healthcare. Meanwhile, each entity within these connected environments carries its unique requirements and individual goals. For connected environments to gain greater legitimacy among end users, service-oriented systems must adopt a new paradigm that allows end users to move from being passive consumers to actively participate in monitoring their own data at different stages of its lifecycle. In this context, a usage model based on ontological reasoning can be integrated within a data provenance mechanism to help create a trust worthy environment. In this paper, we introduce a vision for democratizing service-oriented systems. We discuss potential new directions that need to be pursued in the area of data management. Then, we review existing schemes applied in IoT data provenance and rely on the requirements to discuss their strengths and weaknesses. Finally, we summarize a number of potential solutions to direct future research.

International audience ; Internet of Things (IoT) is one of the key technologies in the industry 4.0 era and promotes the interconnection of numerous data sources in several sectors such as ecology, agriculture, or healthcare. Meanwhile, each entity within these connected environments carries its unique requirements and individual goals. For connected environments to gain greater legitimacy among end users, service-oriented systems must adopt a new paradigm that allows end users to move from being passive consumers to actively participate in monitoring their own data at different stages of its lifecycle. In this context, a usage model based on ontological reasoning can be integrated within a data provenance mechanism to help create a trust worthy environment. In this paper, we introduce a vision for democratizing service-oriented systems. We discuss potential new directions that need to be pursued in the area of data management. Then, we review existing schemes applied in IoT data provenance and rely on the requirements to discuss their strengths and weaknesses. Finally, we summarize a number of potential solutions to direct future research.

The purpose of this paper is to find out how the basis for personal data protection and the urgency of its protection in the era of digital communication now, which is the normative legal research method concluded that personal data is constitutionally protected by The 1945 Constitution in Article 28G paragraph (1) and Article 28H paragraph (4) , as well as by Law, especially Law No. 11 Year 2008 concerning Information and Electronic Transactions in Article 26, where the urgency of protecting personal data as part of Privacy rights/privacy rights because: a. interference with personal data results in financial losses due to fraud, etc. b. the community right now also seems to be more sensitive so that it is easier to file criminal reports and/ or civil lawsuits because there needs to be more certainty about what personal data is protected; and c. disruption of personal data has great potential to cause citizens to feel disturbed and uncomfortable, thereby disrupting government efforts to encourage the development of digital communications reception; where the regulation should be in the form of a Law, the Law on the Protection of Personal Data, which will be a companion to the Information and Electronic Transactions Law, which makes guidelines for details of personal data that need to be protected and need to be included in the provisions criminal offenses relating to certain acts that violate personal data.

Part 2: Storing Data Smartly (Data storage) ; International audience ; With the increased use of Internet, governments and large companies store and share massive amounts of personal data in such a way that leaves no space for transparency. When a user needs to achieve a simple task like applying for college or a driving license, he needs to visit a lot of institutions and organizations, thus leaving a lot of private data in many places. The same happens when using the Internet. These privacy issues raised by the centralized architectures along with the recent developments in the area of serverless applications demand a decentralized private data layer under user control.We introduce the Private Data System (PDS), a distributed approach which enables self-sovereign storage and sharing of private data. The system is composed of nodes spread across the entire Internet managing local key-value databases. The communication between nodes is achieved through executable choreographies, which are capable of preventing information leakage when executing across different organizations with different regulations in place.The user has full control over his private data and is able to share and revoke access to organizations at any time. Even more, the updates are propagated instantly to all the parties which have access to the data thanks to the system design. Specifically, the processing organizations may retrieve and process the shared information, but are not allowed under any circumstances to store it on long term.PDS offers an alternative to systems that aim to ensure self-sovereignty of specific types of data through blockchain inspired techniques but face various problems, such as low performance. Both approaches propose a distributed database, but with different characteristics. While the blockchain-based systems are built to solve consensus problems, PDS's purpose is to solve the self-sovereignty aspects raised by the privacy laws, rules and principles.